The design and implementation of a fuzzy logic controller (FLC) are presented, offering a solution to improve the irrigation of rose crops. The objective is to reduce the water consumption and operative costs, taking advantage of intelligent controllers and environmental characteristics in a specific region. Considering that the main controllable variables that affect the growth of plants are relative humidity (RH) and temperature (T), in this study, these variables are used to create a system whose aim is to provide an adequate amount of water for a rose crop in the State of Mexico. The Mamdani method was used for the FLC design and the membership functions, while the area centroid was considered as the defuzzification strategy. After implementing the FLC proposal using a field-programmable gate array (FPGA) in a domestic greenhouse, integrated by an array of [5 × 3] rose plants under natural restrictions, a reduction of 0.2 L per week with respect to the traditional manual irrigation system was found. The proposed design highlights the technological advantages of using a fuzzy logic-controlled irrigation system over traditional methods.

Research on medicinal plants is essential for their conservation, propagation, resistance to environmental stress, and domestication. The use of organic nutrition has been demonstrated to improve soil fertility and plant quality. It is also important to study the effects of the Basic Cation Saturation Ratio (BCSR) approach, which is a topic where there is currently controversy and limited scientific information. Evaluating the growth and yields of Agastache mexicana subsp. mexicana (Amm) in different environments is crucial for developing effective propagation and domestication strategies. This includes examining warm and subhumid environments with rain in summer in comparison to mild environments with summer rain. Significant differences were observed in the effects of cold, waterlogging, and heat stresses on the plant’s biomass yield and the morphometric-quantitative modeling by means of isolines. The biomass yield was 56% higher in environment one compared to environment two, 19% higher in environment one with organic nutrition, and 48% higher in environment two with organic nutrition compared to using only BCSR nutrition. In the second harvesting cycle, the plants in environment one did not survive, while the plants in environment two managed to survive without needing additional nutrition. Statistical and mathematical analyses provided information about the population or sample. Additionally, further analysis using isolines as a new approach revealed new insights into understanding phenology and growth issues.

In this research, the mathematical model associated with the hydrothermal dehydration process of Nixtamalized Corn Grains (NCG) with different Steeping Time (ST) values, allows the fitting of experimental data with initial moisture $M_0$ and the equilibrium moisture $M_E$ as a function of Isothermal Dehydration Time (IDT). The moisture percentage for any time $t$ and dehydration rate (isolines M(t) and isolines $v_I$ respectively) of the NCG is shown by means of matrix graphics as a simultaneous function of IDT and ST. The relationship between initial dehydration rate $v_0$ and initial moisture $M_0$ establishes as a function of ST. Also, the mathematical model associated with the solution of the second Fick’s law allows calculating the diffusivity rate $v_k$ (H₂O molecules out of NCG) and verify that the rate of change in moisture and the dynamical proportionality constant $k$ has a non-linear dependence on the IDT and that $k$ is directly proportional to $D_{eff}$. The $k$ values strongly relate to ST and the calcium ions percentage into NCG according to solubility lime values into cooking water (or nejayote) as a function of decreasing temperature when ST increases.

This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages --estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).

The Artificial Neural Network (ANN) concept is familiar in methods whose task is, for example, the identification or approximation of the outputs of complex systems difficult to model. In general, the objective is to determine online the adequate parameters to reach a better point-to-point convergence rate, so that this paper presents the parameter estimation for an equivalent ANN (EANN), obtaining a recursive identification for a stochastic system, firstly, with constant parameters and, secondly, with nonstationary output system conditions. Therefore, in the last estimation, the parameters also have stochastic properties, making the traditional approximation methods not adequate due to their losing of convergence rate. In order to give a solution to this problematic, we propose a nonconstant exponential forgetting factor (NCEFF) with sliding modes, obtaining in almost all points an exponential convergence rate decreasing. Theoretical results of both identification stages are performed using MATLAB® and compared, observing improvement when the new proposal for nonstationary output conditions is applied.

Medicinal plants are crucial for developing new medicines; the Agastache mexicana subspecies mexicana (Amm) or “toronjil morado” is considered an at-risk plant because of its inability to grow outside of its natural habitat. This research aims to measure the response of Amm to achieve survival with low nutrient levels and water stress, supported by different dioxide silicon (SiO₂) (0.0, 0.2, 0.4, and 0.8%) and artificial shading (AS) levels (38, 87, and 94%). Applying AS and SiO₂ supported gradual tolerance to the effects of both waterlogging and drought stresses. The survival values were statistically significant in the interaction and simple analysis of SiO₂ and AS, where p = 0.0001. The highest survival percentage (SP) value was SP = 91.3% for treatment number six (τ₆), where AS = 94% and SiO₂ = 0.2%. Additionally, the index of survival efficiency under water stress (SI_ef) was calculated, with the highest value being SI_ef = 0.062 for the hypothetical interaction of AS = 94% and SiO₂ = 0.4. Research has shown that applying both SiO₂ and a high level of AS can guarantee the survival of Amm under water stress.