Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.

Today, agriculture faces many challenges, such as the use of inefficient methods that affect crop quality. Precision agriculture (PA), combined with advanced technologies, improves monitoring, while the integration of wireless communication optimizes processes and resources. This work presents the design of a communication prototype applied in precision agriculture, which allows the acquisition, processing, and wireless transmission of information extracted from the Cotonet pest to The Things Network (TTN) cloud server. This prototype integrates technologies and protocols such as LoRaWAN, Message Queuing Telemetry Transport (MQTT), Internet of Things (IoT) sensors, and Computer Vision. This prototype employs a robust processing and segmentation algorithm, which allows the recognition of pests in citrus plants based on color. The results show that lighting conditions, weather, and time of day influence the quality of the captured images. The relationship between image resolution, brightness, and processing time shows that higher-resolution images (1920 × 1080 pixels per image) provide better detection of pest pixels (greater than 50% of the pest index) but require longer processing time (28.415 ms on average). Furthermore, the developed system effectively detects an index of affection of Planococcus citri (Cotonet) in agricultural plantations through an end-to-end technological implementation that integrates image processing, wireless communication, and IoT technologies.

Hydroponics in recirculation systems faces significant challenges in regulating critical parameters, such as pH and electrical conductivity (EC), especially in adverse environmental conditions, such as high altitudes. This paper presents the design and validation of a fuzzy controller integrated with IoT for NFT-type hydroponic systems, implemented on low-cost hardware and tested in the city of Cusco (3339 m.a.s.l.). Unlike previous studies that are limited to simulations or laboratory tests, the proposal was validated under real growing conditions, demonstrating its practical viability. The system incorporates a fuzzy controller based on simple rules, an IoT module with ESP32 for remote monitoring via Blynk, and an accessible and replicable architecture. The results demonstrate stable performance in pH and EC regulation, with adequate response times, minimal overshoot, and reduced errors, achieving levels comparable to those of higher-cost commercial solutions. The main contribution of this study is the demonstration that an intelligent, economical, and replicable system can be applied in agricultural environments with limited resources, offering a viable alternative for improving productivity in high-altitude hydroponic systems.

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit.

El objetivo principal consistió en identificar la variación del promedio de la satisfacción del cliente en proporción al promedio de la calidad del servicio de internet. La población estuvo conformada por trece clientes de la cartera corporativa del proveedor de servicios de internet América Móvil Perú S. A. C. Asimismo, se utilizaron las variables: calidad del servicio de internet y satisfacción del cliente. Se empleó el diseño no experimental de nivel correlacional, que ha sido desarrollado en la aplicación de las encuestas, las cuales brindaron resultados acerca de la variación entre las variables. La investigación ha concluido con el resultado de 0.77, lo que indica que existe variación proporcional positiva entre ambas variables. Además, se encuentra en un nivel correlacional alto; y, al realizar el análisis del nivel de significancia bilateral de 0.000 menor a 0.05, se concluye que el promedio de la calidad del servicio de internet varía en proporción al promedio de la satisfacción del cliente.

Measuring solar irradiance is key to assessing the conversion efficiency of photovoltaic (PV) modules. Also, PV modules can be used to estimate irradiance through their electrical response to solar radiation using closed-form models (CFMs). This paper presents a prototype design for irradiance estimation based on evaluating three CFMs by implementing a maximum power point tracking (MPPT) system and a surface temperature measurement system. The system employs an incremental conductance (IC)-based control algorithm, which is optimized to eliminate oscillations at the maximum power point (MPP) and ensure efficient MPP tracking. Experimental validation of the implemented circuits is carried out using Arduino Nano, calibrated sensors, and low-cost electronic devices. Tests in real conditions were performed for four days under different irradiance scenarios, using two monocrystalline PV modules: one with 10 years of use and one new one. The accuracy of the CFMs was evaluated using the mean absolute percentage error (MAPE) and root mean squared error indicators, comparing their estimates with measurements from a Davis Instruments pyranometer. The most accurate CFM obtained a MAPE of 4.38% with the 10-year module and 3.26% with the new module. The results show that the proposed methodology provides estimates with an error of less than 5%, which validates its applicability under various climatic conditions, even with old PV modules.