The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

The design and implementation of a low-cost system for monitoring and remote control of a greenhouse using fuzzy logic is presented. For the control system, an Arduino Mega board was programmed with a fuzzy algorithm to monitor and perform control actions for environmental temperature, soil moisture, relative humidity, and lighting. A website was designed to visualize the main indicators of agricultural interest and to get access to tools such as forced ventilation, misting systems, and sprinkler irrigation. For connectivity to the webpage, an Arduino Ethernet Shield was used. Thus, it was possible to establish a local area network and monitor and control the greenhouse climate variables manually or automatically. The application designed allowed access to the configuration, monitoring, and control of climatic conditions in the greenhouse. The effectiveness of fuzzy logic to control nonlinear systems was therefore verified without the mathematical model of the plant. Thus, the use of resources for a gable roof greenhouse prototype was optimized.

This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV) module. A nonlinear autoregressive network with exogenous inputs (NARX) was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA), and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

Se analizan los resultados de la gestión administrativa en las instituciones educativas rurales del distrito de Santa Marta, Colombia, considerando dimensiones como enfoque administrativo, nivel de gestión, usos de investigación de operaciones en educación y toma de decisión. Es una investigación descriptiva, de campo y transversal, con población de 25 instituciones rurales. El instrumento fue un cuestionario tipo escala de Likert con 48 ítems, validado mediante juicio experto y confiabilidad de 0.87 Alfa de Cronbach. La estadística fue descriptiva, y se construyó un baremo de 5 categorías. Los resultados indican que no hay enfoque administrativo predominante en las instituciones educativas de la zona rural y que existe moderada presencia en el uso de técnicas de investigación operacional en la gestión administrativa de los colegios. Además, 72% de los directores no utilizan soportes para decidir, 48% no usa referentes teóricos, aunque existen condiciones para adelantar estudios de investigación operacional en cada institución.

Planning for offshore wind energy projects is intricate due to the consideration of multiple variables in identifying optimal project areas. The primary challenge lies in locating suitable sites, a process that often necessitates extensive feasibility studies spanning several years. The primary goal of this study is to apply the Fuzzy Analytic Hierarchy Process (FAHP) to prioritize criteria and sub-criteria, facilitating decision-making in choosing appropriate locations for offshore wind projects in the Colombian Caribbean Sea. The weights derived from FAHP were utilized in a Geographic Information System (GIS) to analyze the physical characteristics of the Caribbean Sea’s surface. This tool plays a critical role in evaluating and selecting sites that fulfill established criteria, providing a database of indicators and map visualizations. Four criteria were defined: technical, environmental, social, and economic, along with fourteen sub-criteria, which were prioritized through FAHP based on expert judgment. The results revealed that the most relevant sub-criteria were protected areas and wind speed. Utilizing the ArcGIS Pro software, five zones meeting the predetermined criteria were identified, defining the most feasible areas for offshore wind farm installation, located in the departments of Guajira, Magdalena, Atlántico and Bolívar. The GIS–FAHP methods proved to be useful for feasibility analysis.

This paper presents the implementation of a cost-effective didactic prototype, which was designed as a tool for theoretical and practical learning in the biomedical instrumentation area for engineering students. The prototype provides integrated hardware and software components that allow online acquisition, processing, and visualization of electrocardiographic (ECG), electroencephalographic (EEG), electromyographic (EMG), and electrooculographic (EOG) signals, as well as measurements of bio-impedance from the skin. A control system using an Arduino Uno board and the PIC16F877A and PIC18F2550 microcontrollers was implemented. This control system allows selecting the type of module; the lead to be used in the ECG module; the input channel for the EEG, EMG, and EOG modules; and controlling the signal generator for the bioimpedance module. In addition, a graphical interface was developed in LabVIEW, in which all the acquired biomedical signals can be visualized in real time. It is highlighted as a novelty the modular implementation of the prototype, the incorporation of five modules in a single device and the graphical user-friendly interface. The final result is a low-cost device capable of processing and visualizing bioelectric signals through an interface in LabVIEW, which also allows the user to interact with each of the stages.