Ernesto Olvera-Gonzalez

This study presents the design, construction, and evaluation of a modular chamber for the vegetative propagation of cuttings, capable of simultaneously accommodating three plant cuttings at equidistant positions within a compact volume of 40 cm × 45 cm × 15 cm. It integrates distributed sensing using DHT22 temperature and relative humidity sensors, a BH1750 light sensor, and an MQ-2 gas/smoke detection sensor; programmable lighting via four NeoPixel WS2812B boards arranged in two parallel rows; and an ESP32 microcontroller responsible for processing, wireless communication, and threshold-based feedback control of environmental variables. The system was continuously monitored for four weeks to characterize the internal foliar microclimate in relation to external environmental conditions. Internal temperatures ranged from a weekly average of 25.26 °C to 28.71 °C, while relative humidity in the foliar zone varied between 28.99% and 41.24%. The lighting system provided stable and repeatable illuminance conditions, with weekly averages reaching up to 111.10 lux, and the gas sensor enabled continuous detection of transient variations in gas concentration within the chamber. The results demonstrate the feasibility of the proposed chamber as a compact, modular, and replicable platform for environmental data acquisition and experimental control in future studies on plant cutting propagation, biological rooting performance was not evaluated in the present study, therefore, the reported results primarily validate the technical and environmental performance of the developed prototype.

Waste cooking oil is a valuable feedstock for renewable biofuels via hydrodeoxygenation reactions. This study presents a novel strategy with nickel catalysts supported by carbon derived from waste tire rubber (Ni/C). The findings reveal the synthesis of renewable biofuel with diesel-like properties. Under the investigated conditions, optimal yields of 70.12% and 96.5% are achieved for n-C₁₇ and the total C₁₀–C₁₈ alkane fraction, respectively. To achieve them, we conducted 246 hydrodeoxygenation reactions in a stainless-steel batch autoclave with a vertical four-blade agitator; the impeller was operated in alternating rotation at 120 rpm to ensure comprehensive radial and axial mixing. For each one, 0.9 g of the Ni/CTR catalyst was used, varying reaction inputs: system pressure (20–25 bar), active metal loading of the catalyst (5–10 wt.% Ni), isothermal reaction temperature (320–340 °C), and reaction time (4–5 h), obtaining output variables as the molar yields of catalytic n-C₁₇ (dynamic range: 36.78% to 63.03%) and the global yield of the C₁₀–C₁₈ alkane series (50.99% to 88.98%). After that, we trained and evaluated 2500 distinct neural network configurations to identify the optimal architecture. The best model achieved R² values of 0.9525 for the C₁₀–C₁₈ alkane series and 0.9544 for n-C₁₇, with error metrics of MSE, MAE, and MAPE at 0.0019, 0.0388, and 9.54%, respectively. Finally, we conducted 1000 simulations, increasing input ranges and step sizes, varying reaction parameters, and predicting yields using the neural network to identify the maximum alkane production. This led to improvements in n-C₁₇ and total alkane (C₁₀–C₁₈) yields of 11.25% and 8.45%, respectively.

The integration of LED lighting enables precise radiation control in plant factory cultivation systems. While LEDs offer energy efficiency and spectral tuning, achieving a uniform photosynthetic photon flux density (PPFD) remains a critical technical challenge. This study evaluated the impact of three spatial LED configurations on irradiance uniformity using commercial horticultural LEDs and a light recipe of 75% red and 25% blue. Optical simulations in TracePro^® 2017 were conducted to analyze radiant flux, optical efficiency, and uniformity, along with LED quantity, system cost, and electrical consumption under two environmental scenarios: open (without reflective walls) and closed (with reflective walls). Results show that distribution 3, which featured reduced central LED density, achieved 4–8% higher homogeneity in the open scenario, and 2.7–6.5% in the closed scenario, compared to symmetric layouts (distribution 1 and 2). Reflective walls increased average PPFD by up to 20% and optical efficiency by around 9%, with a minimal effect on uniformity. Lowering the lamp-to-canopy distance from 35 cm to 30 cm resulted in a 10% increase in PPFD. Despite a reduction in total photon flux, distribution 3 exhibited superior irradiance homogeneity. One-way ANOVA confirmed significant effects of environment, height, and LED model (p < 0.05), but not of spatial alone. This simulation-based methodology offers a robust framework for optimizing energy-efficient lighting systems. Future work will explore the integrating of non-visible wavelengths and experimental validations to extend practical applicability.

Heavy metal contamination, particularly lead (Pb), represents a threat to ecosystems and human health. This study investigates the variety Aloe vera var. Chinensis as a plant sensing platform for detecting the presence of lead by characterizing its bioelectrical response. A low-cost system based on Arduino was developed to acquire real-time electrical signals from 160 plants, equally divided between two groups: control conditions (n = 80) and Pb acetate exposure (500 mg/L; n = 80). Two recording sessions per plant were obtained after the plant had stabilized, resulting in 320 labeled measurements. The signals were characterized using the discrete wavelet transform (DWT), autoregressive (AR) models, and complexity measures based on entropy. Three classifiers—Support Vector Machine, Random Forest, and XGBoost—were trained and evaluated using five-fold cross-validation and a held-out test set with plant disjoint samples. XGBoost achieved the highest performance (accuracy = 93.0%; precision = 92.5%; recall = 93.8%; F1-score = 93.1%; and 95% CI for accuracy: 90.4–95.2% via bootstrap), significantly outperforming the other models. SHAP analysis revealed that midscale wavelet entropy and energy features, along with AR residual variance, were the most discriminative for Pb detection. These findings demonstrate a scalable, low-cost, and interpretable biosensing framework with potential applications in real-time environmental monitoring and early detection of heavy metal contamination.

The use of chemical pesticides in agriculture has led to the development of resistant pest populations, posing a challenge to long-term pest management. This review aims to evaluate the scientific literature on the individual and combined use of baculoviruses with conventional chemical and biological insecticides to combat Plutella xylostella, Spodoptera exigua, and Spodoptera frugiperda in broccoli, tomato, and maize crops. Notable findings include that both individual Plutella xylostella nucleopolyhedrovirus (PxNPV) and the combination of Plutella xylostella granulovirus (PxGV) and azadirachtin at a low dose effectively control Plutella xylostella; both combinations of Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) with emamectin benzoate and chlorfenapyr reduced resistance in Spodoptera exigua and increased the efficacy of the insecticides; and the combination of Spodoptera frugiperda nucleopolyhedrovirus (SfMNPV) and spinetoram is effective against Spodoptera frugiperda. Integrating baculoviruses into pest management strategies offers a promising approach to mitigate the adverse effects of chemical pesticides, such as resistance development, health risks, and environmental damage. However, there remains a broad spectrum of research opportunities regarding the use of baculoviruses in agriculture.

Vertical farming systems offer an efficient solution for sustainable food production in urban areas. However, managing nitrate (NO₃⁻) levels remains a significant challenge for improving crop yield, quality, and safety. This study evaluated the effects of nitrate availability on growth performance, nutrient uptake, and water use efficiency in a vertical hydroponic system that intercropped lettuce (Lactuca sativa) with alfalfa (Medicago sativa). The experiment was conducted in a controlled vertical hydroponic system using Nutrient Film Technique (NFT) channels, with nitrogen levels set at 0, 33, 66, 100, and 133% of the standard concentration. The results indicated that the intercropping treatment with 66% nitrate (IC-N_66%) improved water use efficiency by 38% and slightly increased leaf area compared to the other intercropping treatments. However, the control group, which consisted of a monoculture with full nitrate supply, achieved the highest overall biomass. Ion concentrations, including nitrate, calcium, magnesium, and micronutrients, were moderately affected by the intercropping strategy and nitrate levels. These findings suggest that moderate nitrate input, combined with nitrogen-fixing legumes, can enhance resource efficiency in hydroponic systems without significantly compromising yield. These findings offer a promising framework for incorporating legumes into hydroponic systems, minimizing the need for synthetic inputs while maintaining yield. These results support the use of agroecological intensification strategies in highly efficient soilless systems.

Lettuce (Lactuca sativa ‘Parris Island’), commonly grown in vertical farms for sustainable production, has been studied for its response to UVA LED light, but its disinfectant potential remains unexplored. Hence, the proposal intends to cover two important aspects, the development of the plant and its disinfection during cultivation, to obtain plants that are ready for consumption. Three LED light treatments were configured: Mode 1 (WBUVA-P) and Mode 2 (WBUVA-C) used White + Blue + UVA (395 nm), with intermittent and continuous application, respectively. The Control applied White + Blue (WB), denoted as Mode 3. A specific evaluation of different parameters, such as disinfection and identification of bacteria, biomass, chlorophyll content (SPAD units), and leaf area (LA), was conducted in the experiment. The most effective results were obtained with Mode 1 (WBUVA-P), achieving a 99.90% disinfection rate and promoting organic matter accumulation, as shown by increased leaf area, fresh weight, and dry weight. In contrast, Mode 2 (WBUVA-C) reached a 99.00% disinfection rate but did not significantly impact organic matter compared to the Control. These results suggest that UVA-LED radiation can be a valuable tool for food production, enhancing disinfection and organic matter content. However, further studies are needed to explore different intermittent UVA-LED emission durations and test other wavelengths.

El objetivo de esta investigación fue analizar la relación entre la gestión del conocimiento y el empoderamiento en mujeres universitarias, explorando los indicadores clave que los vinculan y evaluando el impacto de la maternidad como factor moderador. Las participantes fueron 262 mujeres estudiantes de Ingeniería Industrial en el Instituto Tecnológico de Aguascalientes. La investigación de tipo cuantitativa, no experimental, transversal y correlacional, se llevó a cabo mediante la aplicación virtual de un cuestionario con escala de Likert aplicado de manera virtual. Los datos fueron analizados con SPSS mediante pruebas de normalidad (Shapiro-Wilk), homogeneidad de varianza (Levene), correlación de Pearson y un modelo de regresión lineal múltiple. Los resultados mostraron una correlación positiva y significativa entre la gestión del conocimiento y el empoderamiento (R=0.430). El modelo de regresión explicó el 22.3% de la varianza del empoderamiento, identificando a la gestión del conocimiento como el predictor más relevante (Beta=0.432), seguido por la maternidad como predictor secundario (Beta=0.196). Se concluye que la gestión del conocimiento fomenta el liderazgo, la autonomía y las habilidades críticas en las mujeres universitarias, constituyéndose como un factor esencial para su empoderamiento. La maternidad, aunque influyente, puede limitar el acceso a procesos de empoderamiento, lo que subraya la necesidad de políticas institucionales inclusivas que equilibren responsabilidades personales y desarrollo académico.

Oleic acid is a valuable molecule for biofuel production, as it is found in high proportions in vegetable oils. When used, oleic acid undergoes hydrodeoxygenation reactions and produces alkanes within the diesel range. These alkanes are free of oxygenated compounds and have molecular structures similar to petrodiesel. Our research introduces a novel approach incorporating oleic acid into the hydrodeoxygenation process of Ni/Tire Rubber Carbon (Ni/C_TR) catalysts. These catalysts produced renewable biofuels with properties similar to diesel, particularly a high concentration of n-C₁₇ alkanes. Moreover, our Ni/C_TR catalyst produces n-C₁₈ alkanes, but the generation of n-C₁₈ alkanes typically requires more complex catalysts. Our procedure achieved 74.74% of n-C₁₇ alkanes and 2.28% of n-C₁₈ alkanes. We used Box–Behnken and artificial neural networks (ANNs) to find the optimal configuration based on the predicted data. We developed a dataset with pressure, temperature, metal content, reaction time, and catalyst composition variables as inputs. The output variables are the n-C₁₇ and n-C₁₈ alkanes obtained. ANN602020 was our best model for obtaining the peak response; it accurately forecasted the n-C₁₇ and n-C₁₈ generation with R2 scores of 0.9903 and 0.9525, respectively, resulting in an MSE of 0.0014, MAE of 0.02773, and MAPE of 2.03979%. The combined R² score for both alkanes was 0.97139.

Vertical farming (VF) refers to systems of agriculture where crops are grown in trays stacked vertically by exposing them to artificial light and using sensing technology to improve product quality and yield. In this work, we propose an advanced filtering scheme based on recurrent neural networks (RNNs) and deep learning to enable efficient control strategies for VF applications. We demonstrate that the best RNN model incorporates five neuron layers, with the first and second containing 90 long short‐term memory neurons. The third layer implements one gated recurrent units neuron. The fourth segment incorporates one RNN network, while the output layer is designed by using a single neuron exhibiting a rectified linear activation function. By utilizing this RNN digital filter, we introduce two variations: (1) a scaled RNN model to tune the filter to the signal of interest, and (2) a moving average filter to eliminate harmonic oscillations of the output waveforms. The RNN models are contrasted with conventional digital Butterworth, Chebyshev I, Chebyshev II, and elliptic infinite impulse response (IIR) configurations. The RNN digital filtering schemes avoid introducing unwanted oscillations, which makes them more suitable for VF than their IIR counterparts. Finally, by utilizing the advanced features of scaling of the RNN model, we demonstrate that the RNN digital filter can be pH selective, as opposed to conventional IIR filters.