This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangements. By employing a linear additive model to solve a multi-criteria decision model, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare and installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in MATLAB R2023b, with results validated through simulations in DIALux evo 12.0. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

This paper presents the development of an energization system prototype for IoT sensor nodes using Peltier cells as energy harvesters; its operation is optimized by applying a maximum power point tracking algorithm (MPPT) to capture as much electrical energy as possible, even if the cell temperature conditions have variations. In the IoT sensor node, a power management algorithm that works in accordance with the measurement and transmission operations can extend the node operating time, to obtain a greater amount of information and reducing the need for battery maintenance. The proposed methodology consists of developing an energization system, as well as the IoT sensor node. The energization system consists of a block of Peltier cells to obtain up to 4 V, a SEPIC-type DC-DC converter, and a 3.7 V lithium battery for energy storage. The converter works in a closed loop with the MPPT algorithm and delivers a voltage that guarantees the maximum power transfer to the battery. The sensor node was developed based on the ESP8266 development board, it allows data acquisition of temperature, humidity, light intensity, presence, and sound. The node transmits this information to the Ubidots platform for real-time visualization; to take advantage of its processing capacity, MPPT and energy management algorithms are also implemented. The results showed that to obtain a minimum voltage of 3.3 V in the energization system, a temperature difference of $59\pm 1$ °C between the plates of the Peltier cells is required. The MPPT algorithm allows working at the maximum power point and keeps the power delivered to the battery stable, with small transients when the information is transmitted; however, the overshoot and the settling time are reduced and do not affect the node operation.

En este artículo se desarrolla un prototipo de cultivo hidropónico NFT con un sistema de control de inyección de nutrientes que contiene la solución acuosa que alimenta un cultivo hidropónico NFT de lechugas como una alternativa de producción para la creciente demanda de alimentos, sustituyendo el uso de suelo por una solución acuosa para alimentar al cultivo. Se utiliza un controlador de entrada mediante un sistema de visión artificial potenciado por una red neuronal entrenada para la identificación de la etapa de crecimiento de las plantas de todo el lote de producción, de manera que establece el nivel adecuado de nutrientes de la solución acuosa, este valor hace de referencia para un sistema de control en lazo cerrado que regula los actuadores del sistema de inyección hasta alcanzar el nivel adecuado de ph, mediante el cual se miden los nutrientes contenidos en la solución acuosa. Los resultados obtenidos permiten una regulación precisa del nivel de pH del agua con variaciones mínimas en un rango de 5,5 - 6.5 en todas las etapas de crecimiento de las lechugas, además de un 93.33\% de precisión en la detección del tiempo de maduración y cosecha. Esto permite disponer de un sistema de cultivo sustentable, que optimiza el uso de nutrientes y suelo mediante la recirculación y la estructura de cultivo vertical, así como se reduce el consumo energético al activar el sistema cuando es necesario una regulación.