Este trabajo presenta un análisis estructural de un prototipo de bastidor para Go-Kart, desarrollado mediante el método de elementos finitos (FEA), con el propósito de evaluar su comportamiento mecánico frente a cargas estáticas y dinámicas. El diseño se fundamentó en las dimensiones estándar de la CIK-FIA, 700 mm de ancho, 1800 mm de largo y 1070 mm de distancia entre ejes, utilizando tubería de acero ASTM A36, diámetro de 38.1 mm y espesor de 3 mm, material seleccionado por su disponibilidad y relación costo-resistencia en el mercado ecuatoriano. Se modeló un bastidor tipo escalera, aplicando cargas equivalentes al peso del conductor, los componentes y las solicitaciones dinámicas generadas por frenado, aceleración y maniobras de giro. La calidad de la malla fue validada mediante el criterio del cociente de aspecto, con un valor de 3.266, lo que aseguró una buena calidad numérica. Los resultados indicaron que, tras incorporar un larguero transversal adicional, los esfuerzos máximos alcanzaron 232 MPa, permaneciendo por debajo del límite elástico del material de 240 MPa, mientras que la deformación máxima fue de 0.8 mm. Estos resultados confirman la viabilidad estructural del diseño propuesto y destacan la utilidad del análisis computacional como herramienta para optimizar el diseño de bastidor. Se propone esta metodología como base para futuras validaciones experimentales, estudios de fatiga e implementación de materiales alternativos que contribuyan a mejorar la seguridad y eficiencia estructural en vehículos de pequeña escala.

En la industria del petróleo y gas la aplicación de metodologías de predicción y estimación como las Redes Neuronales Artificiales ha representado una herramienta valiosa en todo el sistema petrolero. El objetivo de la revisión fue analizar, desde la perspectiva bibliométrica, la producción científica de los últimos cinco años sobre el uso de las Redes Neuronales Artificiales en esta industria. Se utilizó la base de datos de Scopus, filtrando la información por tiempo, temática, tipo de documentos y origen. Se utilizó una ecuación de búsqueda con palabras clave artificial neural network y oil and gas industry. La información se procesó mediante los softwares LibreOffice Clac, JASP y VOSviewer. Se obtuvo un total de 267 documentos, de los cuales el 59,9% correspondió a artículos científicos originales y el 56,0% fueron publicados en revistas científicas. China se posicionó como el país con mayor producción, mientras que los principales autores procedieron de Arabia Saudita. El 69,8% de las investigaciones se enfocaron en la subárea de Energía, y el 67,1% estuvieron afiliadas a empresas petroleras. Las áreas de aplicación incluyeron, además de las tradicionales (exploración, perforación, producción y yacimientos), el análisis de flujo, levantamiento artificial, emisiones, detección de anomalías, automatización, corrosión y detección de dióxido de carbono. Se concluye que, en los últimos cinco años, se ha profundizado la investigación sobre el uso de las RNA (redes neuronales artificiales) en la industria del petróleo y gas, especialmente en temas como la predicción de producción, reservas y estudios de yacimientos.

The exponential increase in Internet traffic is mainly due to the proliferation of services such as audio and video streaming, the emergence of applications that require a lot of bandwidth to work optimally and generally the process of digitalization of services. In this context, bandwidth management plays a fundamental role, which translates into a better experience for users. Traffic congestion causes the exchange of information to become deficient, that is why techniques such as automatic bandwidth adjustment have been investigated, which manages the bandwidth according to the traffic demand, therefore in this document a study is made about the automatic bandwidth adjustment, the way in which Artificial Intelligence is integrated with computer networks, finally a comparison will be made of several machine learning methods, cataloged within supervised learning, carrying out several experiments determining that Random Forest is the most effective method to predict the automatic bandwidth adjustment, followed by Naive Bayes, Logistic Regression, and Support Vectorial Machine (SMV), on the other hand K -nearest neighbor (KNN) and neural network do not demonstrate considerable effectiveness, each experiment was carried out taking into account the Quality of Service (QoS).

The purpose of this research was to determine the quality of water for bovine consumption on livestock farms in the El Progreso parish - Galapagos by applying a quality index following the Brown methodology proposed by the National Sanitation Foundation of the United States. Information was collected from the livestock farms through a socio-environmental diagnosis of the area, where five localities were considered: San Joaquín, Las Goteras, Cerro Verde, Puerto Chino and El Progreso from which fifteen livestock farms were selected for the respective sampling of the sources. of water. Therefore, nine parameters were considered for the analysis of the respective water sources: pH, TDS, Fecal Coliforms, electrical conductivity, hardness, nitrites, phosphates, temperature and turbidity. Of the total livestock farms monitored, three of them presented a rating of Good quality in a range of 70 to 90, while the remaining farms recorded values with a Medium classification, which are those that recorded AQI values between 51 to 70 according to the criteria established by the NSF. This research highlights the importance of frequently carrying out analyzes of the water sources from which cattle drink to determine the quality of the resource, and also provide technical advice to ranchers in the area on the management and cleaning of the reservoirs.

The research addresses the study of heat exchange between heavy crude oil and the environment during surface pumping, specifically of a crude oil with an API gravity of 17.5, under the particular atmospheric conditions of Eastern Ecuador. The main objective of the study is to evaluate the temperature loss in a 50-meter segment of SCH-80 pipe, with a diameter of 4 inches, used for the transportation of heavy crude. This aims to understand how heavy crude loses temperature and to determine the convective coefficient, knowing that the heat loss from the fluid to the environment occurs mainly by convection. This is to determine what the temperature losses will be in longer pipe sections. For this purpose, a methodology using computational fluid dynamics (CFD) simulation was employed, a key tool for predicting the thermal behavior of crude in interaction with the environment. It was determined that the convective coefficient is 52 W/m2.K, and there is a temperature loss of 3.2 K in the 50-meter section. With this data, future research could evaluate potential heating technologies that facilitate the transport of heavy crude oil. This approach would allow for exploring innovative solutions to improve efficiency and effectiveness in managing heavy crude, facing one of the main challenges in its transport: managing its high viscosity

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations > 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

The Internet of Things (IoT) has the potential to revolutionize healthcare by enabling remote patient monitoring, personalized care and disease prevention. In Ecuador, research on IoT in the healthcare field is rapidly expanding. However, there’s a need for a clearer understanding of the current state of this research. This study examines the contributions of Ecuadorian authors in this field through their publications in two globally impactful bibliographic databases. The methodology employed is a systematic review using the PRISMA statement, resulting in a final stage comprising 23 articles. These publications encompass system proposals, prototypes, and reviews with applications in areas such as epidemiology, cardiology and nursing. The recurrent mention of patient information privacy is a challenge in implementing IoT-based healthcare systems. The conclusions emphasize that future work perspectives should address the challenges identified, considering the growing trend of publications from Ecuadorian authors.

In this investigation, the comparative study of the superficial hardness of AISI 8620 steel thermally treated by carburizing, using vegetable and coking coals, was developed. The objective was to propose an alternative to the element used to generate the carburizing atmosphere. The previous stages have been described, such as the preparation of the material and its verification, as well as the dimensions and characteristics, defined by the ASTM E-18 standard. By considering four variables, each one with two levels, an orthogonal array matrix of 16 experiments was generated. The surface hardness measurements were processed through an analysis of variance (ANOVA), where the significance of each variable considered as a factor was obtained. Coking coal can be used as an element for the generation of carburizing atmosphere since a maximum relative error of 3.86 % was obtained between the experimental values and the values in the literature. In contrast, the vegetable coal did not improve the mechanical properties of the steel, reaching an average hardness of 25.95 HRC after heat treatments.

Nowadays, the measurement of respiratory dynamics is underrated at clinical setting and in the daily life of a subject and it still represents a challenge from a technical and medical point of view. In this article we propose a concept to measure some of its parameters, such as the respiratory rate (RR), using four inertial sensors. Two different experiments were performed to validate the concept. We analyzed the most suitable placement of each sensor to assess those features and we studied the reliability of the system to measure abnormal parameters of respiration (tachypnea, bradypnea and breath holding). Finally, we measured post-COVID-19 patients, some of them with breath alterations after more than a year of the diagnosis. Experimental results showed that the proposed system could be potentially used to measure the respiratory dynamics at clinical setting. Moreover, while RR can be easily calculated by any sensor, other parameters need to be measured with a sensor in a particular position.