Luis Octavio Solís-Sánchez

Urban LoRaWAN deployments are engineering systems whose connectivity, energy use, reliability, and operating cost depend on informed gateway-placement and configuration decisions. Although LoRa is widely adopted for smart-city and industrial Internet of Things (IoT) applications, the methods used to construct gateway coverage analyses remain fragmented across propagation modeling, field measurement, simulation, optimization, and emerging data-driven approaches. This technical survey critically synthesizes the representative literature on methodologies for LoRa gateway coverage analysis in urban environments. Its contributions are threefold: it organizes the evidence around performance evaluation, configuration and optimization, localization, applications, communication technologies, security, and comparison with alternative LPWANs; it maps those methods onto a system-level workflow linking inputs, inference, decisions, and validation; and it identifies reporting and validation conditions needed for transferable engineering evidence. The cited literature prominently represents empirical measurements and simulation models, whereas integrated intelligent-system workflows that combine urban-context data, real-time measurements, predictive models, and decision support remain underdeveloped. Inconsistent environmental assumptions, incomplete reporting of validation settings, and nonuniform performance metrics limit transferability between cities. This survey therefore formulates a system-level design framework and research agenda centered on digital representations of the radio environment, hybrid physics- and data-driven models, adaptive configuration, and measurable validation of reliability, energy efficiency, and cost. These findings will support researchers and engineering practitioners in designing trustworthy, scalable, and adaptive IoT connectivity systems for urban environments.

This study assessed the effect of training record length on daily pan evaporation estimation using Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) models in a semi-arid region of Mexico. Two historical data scenarios comprising 10 and 20 years of daily observations were evaluated using different combinations of temperature, relative humidity, wind speed, and solar radiation variables. Both scenarios followed chronological training and validation periods and used the same independent testing period (2023–2024). The 10-year scenario used 2015–2020 for training and 2021–2022 for validation, whereas the 20-year scenario used 2005–2018 and 2019–2022, respectively. Among the selected configurations, MLR - M5 showed the most favorable overall performance for the 10-year scenario, with an RMSE of 1.98 mm/day, while LSTM - M9 was selected as the reference configuration for the 20-year scenario, achieving 1.97 mm/day. Extending the training record reduced LSTM RMSE by 0.12 mm/day (5.74%), whereas MLR RMSE increased by 0.05 mm/day (2.53%). Notably, LSTM using 20 years achieved an RMSE only 0.01 mm/day (0.51%) lower than MLR using 10 years. These results indicate that increasing the training record does not guarantee uniform improvements and that its effect depends on the learning paradigm and estimation configuration.

This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is deployed on the physical system, produces an experimental dataset, and is evaluated through a performance index. The objective function combines tracking error, control effort, and control-signal variation, while penalty terms identify and penalize actuator saturation, excessive overshoot, settling-time violations, steady-state error, and divergent responses. Candidate controllers are generated using a constrained Gaussian Cross-Entropy Method and evaluated directly on the physical actuator. The proposed host-embedded architecture separates two computational time scales: PID execution, encoder processing, and data acquisition are performed in real time on the embedded platform, whereas population sampling, candidate ranking, and distribution updates are executed on the host computer between experiments. Within the reported experimental campaign, the sampling distribution progressively concentrates toward gain regions associated with lower closed-loop cost under the prescribed admissibility criteria. The resulting framework provides a structured and traceable procedure for physical controller deployment, data acquisition, constrained performance evaluation, and episodic PID retuning without explicit plant identification. The reported results demonstrate the feasibility of the architecture on the considered platform, without implying comparative superiority, run-to-run statistical convergence, or analytical closed-loop stability.

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation.

SnO₂ thin films were deposited by spray pyrolysis on glass substrates at a relatively low temperature of 200 °C. This temperature is considerably lower than those commonly reported in the literature for the growth of crystalline SnO₂ films. Atomic force microscopy (AFM) revealed that the surface morphology strongly depends on the number of spray cycles applied during deposition. When a small number of sprays is used (3–5 cycles), self‑assembled circular structures resembling doughnuts are formed on the substrate surface. Increasing the number of sprays progressively covers these structures and results in smoother and more uniform films. The average diameter of the observed structures is approximately 2–10 μm depending on the number of sprays. Structural and vibrational analyses performed by grazing‑incidence X‑ray diffraction (GIXRD) and Raman spectroscopy confirm the formation of tin oxide and provide evidence of structural evolution as the number of spray cycles increases.

Agricultural producers need affordable tools to estimate reference evapotranspiration (ET₀) in field conditions, especially in regions with limited access to complete weather data. In this study, the ET₀ for an alfalfa (Medicago sativa) crop was estimated using the SEG-SRM-V1 electronic system, based on the surface renewal method and validated using the FAO-56 Penman–Monteith method. The estimated ET₀ values for alfalfa ranged from approximately 4.0 to 8.5 mm day⁻¹ under the prevailing climatic conditions: periods of high temperature (21 °C to 33 °C), as measured by the system in the experimental area, with cloud cover, wind (1 to 8 m/s) and net radiation of 664 W/m² to 910 W/m². Comparisons between the two methods yielded determination coefficients of between 0.65 and 0.85, and the values of the errors (MSE, RMSE and MAE) tend to 0, which indicates that the estimates of ET₀ measured by the system (SEG-SRM-V1) are close to those obtained using the Penman–Monteith method. Similarly to the performance of open-field systems operating under atmospheric and vegetation cover conditions, these results demonstrate that high-frequency (10 Hz) air temperature measurements provide sufficient physical information to support the estimation of ET₀ in alfalfa, while the open architecture of the SEG-SRM-V1 system allows for flexibility and scalability for irrigation management applications in other crop types.

Phase-angle AC control is a low-cost technique for regulating power in resistive loads, but its performance depends on accurate trigger timing. This study quantitatively compares an ESP32-based phase-angle controller implemented in MicroPython and in native C using ESP-IDF. Firing delay was measured over 1000 consecutive cycles at firing angles from 10° to 150° under a 60 Hz supply, and the timing error was converted into equivalent angular deviation. The native C implementation reduced the mean timing error from 218.2–234.7 μs in MicroPython to −10.3–6.1 μs after calibration, corresponding to an average improvement of approximately 225 μs or 4.86° across the tested angles. In the current dataset, the measured standard deviation remained angle-dependent and numerically similar in both environments, ranging from 2.5 to 10.1 μs. Oscilloscope measurements confirmed the expected phase-angle operation and the practical timing displacement between firmware strategies. The results show that the principal advantage of the native implementation is improved absolute synchronization accuracy, whereas the residual short-term jitter remains dominated by the shared detection and triggering chain.

In precision agriculture, identifying pests such as the whitefly (Bemisia tabaci) is a significant challenge, as precise knowledge of these insects is essential for developing effective Integrated Pest Management (IPM) strategies. Automated daily monitoring within IPM programs optimizes the diagnostic registration stage by reducing logistical expenses and manual errors, enabling early pest treatment interventions and providing quantitative data for informed decision-making. In this study, an image bank was processed using a Quaternionic Gabor Filter (QGF) algorithmto highlight textural features through hypercomplex correlation. The highlighted objects were then processed by a YOLOv8 pretrained model to identify Bemisia tabaci. Experimental results demonstrate that this combination achieves a precision of 0.868 and an mAP@0.5 of 0.950, while a PSNR of 34.10 dB ensures the structural integrity of the enhanced images. Although the total execution time averages 2.3 s per image due to preprocessing complexity, the GPU inference time of 10.3 ms confirms the potential for high-speed detection. This approach significantly enhanced the morphological features of Bemisia tabaci, increasing the robustness of the detection model and narrowing down processing conditions for yellow trap samples to strengthen precision in the semi-arid regions of Zacatecas, Mexico.

La enseñanza y el mantenimiento tradicional de la maquinaria industrial están basados en manuales, lo que dificulta el desarrollo de habilidades prácticas y la comprensión de componentes internos. Esto tiene como consecuencia el aumento de incidentes operativos, desgaste físico de la maquinaria y cortes por mal manejo. El objetivo principal de este proyecto consiste en diseñar un gemelo digital de una rebanadora de carne con realidad aumentada para facilitar el aprendizaje. Primero, se diseñó un modelo tridimensional de la rebanadora de carne con Blender® basado en la máquina física; luego, se importó a Unity®, un motor de realidad aumentada multiplataforma y compatible con Vuforia®, una plataforma de RA especializada que permite reconocer imágenes, objetos y espacios reales para superponer contenido digital. Se creó un objetivo de imagen para la geolocalización del modelo 3D en el entorno físico. El resultado fue una aplicación que permite a los usuarios manipular y visualizar la rebanadora, así como sus componentes y nombres, en realidad aumentada, simulando el proceso de despiece de forma segura e interactiva. Se espera que esta herramienta facilite el proceso de aprendizaje de los ingenieros especializados en maquinaria industrial, en comparación con los métodos tradicionales, reduciendo la brecha entre la teoría y la práctica, a la vez que minimiza los riesgos operativos. Este enfoque demuestra el potencial que existe entre los gemelos digitales y la realidad aumentada, proporcionando un recurso didáctico moderno para la formación técnica segura de los estudiantes.