The present study focuses on wind power forecasting (WPF) models based on deep neural networks (DNNs), aiming to evaluate current approaches, identify gaps, and provide insights into their importance for the integration of Renewable Energy Sources (RESs). The systematic review was conducted following the methodology of Kitchenham and Charters, including peer-reviewed articles from 2020 to 2024 that focused on WPF using deep learning (DL) techniques. Searches were conducted in the ACM Digital Library, IEEE Xplore, ScienceDirect, Springer Link, and Wiley Online Library, with the last search updated in April 2024. After the first phase of screening and then filtering using inclusion and exclusion criteria, risk of bias was assessed using a Likert-scale evaluation of methodological quality, validity, and reporting. Data extraction was performed for 120 studies. The synthesis established that the state of the art is dominated by hybrid architectures (e.g., CNN-LSTM) integrated with signal decomposition techniques like VMD and optimization algorithms such as GWO and PSO, demonstrating high predictive accuracy for short-term horizons. Despite these advancements, limitations include the variability in datasets, the heterogeneity of model architectures, and a lack of standardization in performance metrics, which complicate direct comparisons across studies. Overall, WPF models based on DNNs demonstrate substantial promise for renewable energy integration, though future work should prioritize standardization and reproducibility. This review received no external funding and was not prospectively registered.

This research presents an identification approach for nonlinear systems based on a NARMAX polynomial model applied to an active magnetic bearing (AMB) prototype, which shows a complex dynamic behaviour, therefore a mathematical analysis of its dynamics was made, obtaining initial parameters for the NARMAX structure, the terms and coefficients that conform the mentioned model were identified by relating the voltage inputs to the system, the current states of the system and the distance of rotor deviation from the central axis, using the Forward Regression Orthogonal Least Square (FROLS) and Recursive Least Square (RLS) algorithms respectively. An infrared sensor and two current sensors were used for data acquisition, and the Kalman filter was also used due to the noise signals obtained.

El Internet de las cosas es un concepto de interconexión digital de objetos cotidianos con Internet, el cual se emplea generalmente para mejorar el nivel de comodidad en las tareas cotidianas y optimizar negocios. Así también, puede ser integrado en el mundo de la salud para darle aplicaciones médicas siendo uno de los escenarios típicos de aplicación es el servicio de telemedicina, como la práctica de la atención médica a distancia. Es así que este artículo describe el desarrollo de un sistema de monitoreo de signos vitales: pulso, temperatura y saturación de oxígeno en la sangre; el cual se basa en la comparación y selección de componentes por medio de la metodología de la investigación, y su validación a través de un análisis estadístico. Dicho sistema de monitoreo busca ser utilizado en el cuidado de pacientes con tratamientos domiciliarios, como un sistema compacto y de bajo costo, con la capacidad de brindar información médica oportuna e implementar a futuro tecnologías de Computación en la nube e Internet de las Cosas para realizar un monitoreo remoto de los datos objetivos y subjetivos del paciente. Además, se detalla el diseño e implementación de la tarjeta principal, la selección de los sensores biomédicos, elaboración de soportes y programación.

High-level noise represents a risk for health, thus an important trouble to handle. For low frequency noise generally it is employing an Active Noise Control (ANC) system. ANC systems operate generating a secondary anti-noise signal according to the superposition principle. Nevertheless, anti-noise signal generation requires techniques such as Filtered-X Least Mean Square (FxLMS) adaptive control algorithm. In this paper, a Single Frequency ANC system is evaluated to obtain an optimal step size to be employed in a Delayed FxLMS Algorithm in order to improve the system time response; thus, analysis, simulations and experiments for a prototype of Single Frequency ANC system are discussed.

Telemedicine has gained significant relevance by allowing patients to be attended to at any time and place, reducing the cost and time of medical care. Therefore, this research aimed at the development of a remote monitoring system for vital signs using cloud computing. Through surveys and expert input, specific system requirements were identified. The chosen sensors, MAX30102 and MLX90614, accurately captured pulse, oxygen saturation, and body temperature. The Arduino Mega-Embedded board facilitated data acquisition, while the Raspberry Pi Zero W enabled remote data transmission. The ThingSpeak platform was used for communication, and the system architecture was established through hardware and software tests. The system’s functionality was verified through expert judgment and statistical tests, showing no significant differences compared to commercial equipment. The remote sampling time for vital sign data was approximately 117 seconds. Overall, this study successfully implemented a robust and efficient remote monitoring system for patients undergoing home treatment.

Engines operations usually produce intense noise and may cause hearing injuries. Therefore, passive absorbers are being used to reduce or eliminate unwanted noise. However, this mechanism is connected with high costs and space requirements. Active Noise Control (ANC) allows to eliminate mentioned drawbacks and therefore offers a better solution. ANC can be designed by feedback, feedforward or hybrid algorithm. Hybrid algorithm has an average settling time, but allows a good response in presence of disturbances. In this paper a fair noise reduction is shown for the use case where an identification process is being made inside of the main hybrid control algorithm loop (online).