In Colombia, agricultural exports have become notoriously prevalent in recent years, causing the creation of new methods capable of increasing production in order to meet the global demands. A very efficient option is the use of greenhouses, given their low building cost, ease of construction, ability to protect crops from natural phenomena and plagues, and the possibility to keep the internal temperature steady during day and night, thus allowing crops to grow fast and healthy. Nowadays, advancements in electronics have allowed boosting the positive effects of these environments, which is why this document introduces a procedure for the implementation of an automated pyramid-type greenhouse, utilizing techniques related to Precision Agriculture (PA) and based on concepts related to the Internet of Things (IoT) for remote monitoring through emerging communication technologies such as the NFRL2401 cards and the Arduino Nano and Mega boards. Inside the greenhouse, variables such as temperature and ambient humidity are measured and controlled via the PCE-P30U Universal Input Signal Converter Data Logger, while ground humidity is monitored by ZD510 capacitive sensors. Outside, variables such as temperature, ambient humidity, negative and positive pressure, and wind speed are measured. Data obtained is taken wirelessly to the server using Windows Server 2019 Datacenter, with Broker MQTT EMQ-X services and MYSQL databases, providing a suitable and efficient environment for agricultural research processes. With the procedure developed in this document, a baseline is proposed for the implementation of a smart greenhouse that can be replicated and used as a test system for smart sowing processes, adapting to the different climate and production conditions of the country.

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

Context: Nowadays, inventory management poses a challenge given the constant demands related to temporality, geographic location, price variability, and budget availability, among others. In neighborhood shops, this process is manually done based on experience (the data generated are ignored), which is sometimes not enough to respond to changes. This shows the need to develop new strategies and tools that use data analysis techniques. Method: Our methodology predicts the weekly demand for 14 common products in neighborhood stores, which is later refined based on investment capital. The method is validated using a database built with synthetic information extracted from statistical sampling. For the prediction model, three supervised learning models are used: support vector machines (SVM), AutoRegressive models (Arx), and Gaussian processes (GP). This work proposes a restricted linear model given an inversion and the predicted quantity of products; the aim is to refine the prediction while maximizing the shopkeeper's profit. Finally, the problem is solved by applying an integer linear programming paradigm. Results: Tests regarding the prediction and inventory adjustment stages are conducted, showing that the methodology can predict the temporal dynamics of the data by inferring the statistical moments of the distributions used. It is shown that it is possible to obtain a maximum profit with a lower investment. Conclusions: Our method allows predicting and refining inventory management in a neighborhood store model where quantities are managed to maximize the shopkeeper's profits. This opens the way to explore this approach in a real scenario or to introduce new techniques that can improve its performance.

Objective: The objective of this paper is to propose a methodology for the optimal location and sizing of D-STATCOMs within a distribution electrical system, with the aim to reduce the annualized operating costs related to the annual power energy losses and the investment costs associated with the installation of the D-STATCOM. Context: This paper presents a hybrid methodology based on a master-slave strategy and the genetic and particle swarm optimization algorithms for solving the problem of optimal location and sizing of Distribution Static Compensators (DSTATCOMs), for reactive compensation in electrical distribution systems. Methodology: In this paper was used a mathematical formulation that represents the effect of the location and sizing of D-STATCOMs in electrical distribution systems; by proposing a master-slave methodology combining the genetic algorithm and the particle swarm optimization algorithms as a solution method. Furthermore, with the aim to validate the effectiveness and robustness of the proposed methodology in this work, three comparison methods, two test systems, and multiple technical considerations were used to represent the electrical distribution systems in a distributed energy resource environment. Results: The results obtained show that the proposed methodology is the most effective solution method for solving the problem, by achieving the greatest reduction in relation to the investment and operating costs. This methodology will allow the grid operators to identify the location and size of the D-STATCOMs within the electrical energy distribution system, with the lowest investment and operating costs in relation to other works reported in specialized literature. Conclusions: The obtained results demonstrate that GA/PSO achieved the best performance, with the DCVSA comparison method in second place, and the GAMS solvers in third place. It is important to notice that it was not possible to evaluate the GAMS solvers on the 69 bus test system, because this solver failed the mathematical formulation that represented this electrical system. Based on previous results, it can be concluded that the GA/PSO is the most suitable optimization method used for solving the problem of optimal integration of D-STATCOMs in Distribution electrical systems for the grid.

This research tested the impact on the soil when implementing a Clean Intensive Livestock Production System (CILPS) via Voisin Rational Grazing (VRG). The methodology consists of the characterization of soil-grass-cattle management, including the soil chemical and biological analysis in the case study farm and reference land (comparison between extensive and CILPS-VRG systems). In addition, the VRG impact on agroecological management was evaluated by calculating the Soil Organic Matter Indicator (SOMI). Once the samples were processed, the T-Student test was applied, verifying the homogeneity of variances (F-Fischer) for the chemical variables and the Kruskal-Wallis test for the biological variables. The results identified substantial differences in some edaphic parameters such as organic matter, percentage of organic carbon, nitrogen, sulfur, and calcium. Moreover, to diversify nematodes and differences in the Rhabdhitids sp., the process characterized products from the natural organic fertilization in the soil with VRG. Finally, the calculation of the SOMI with a value of 41.6 for VRG indicates soil with no alteration or natural balance. Este trabajo evaluó los resultados de la implementación de un Sistema de Producción Ganadero Intensivo Limpio (SPGIL) en el suelo, con Pastoreo Racional Voisin (PRV). La metodología se plantea desde la caracterización del manejo del suelo-pasto-ganado hasta el análisis químico y biológico del terreno en la finca estudio de caso y predio de referencia (comparación entre sistemas extensivo y SPGIL-PRV). Además, se evaluó el impacto del PRV en el manejo agroecológico, calculando el Indicador Acumulativo del Suelo (IAS). Una vez procesadas las muestras, se aplicó la prueba T-Student verificando la homogeneidad de varianzas (F-Fischer) para la variables químicas y la prueba Kruskal-Wallis para la variables biológicas. Los resultados identificaron diferencias significativas en algunos parámetros edáficos como materia orgánica, porcentaje de carbono orgánico, nitrógeno, azufre y calcio; además, mayor diversidad en nematodos y contrastes relevantes en la especie Rhabdhitids sp., resultados de la fertilización orgánica natural producida en el suelo con PRV. Por último, se estableció el cálculo del IACS con un valor de 41,6 para PRV, lo que indica un suelo inalterado y equilibrado naturalmente.