Recent advances in edge computing and Tiny Machine Learning (TinyML) have enabled the deployment of artificial intelligence models directly on microcontrollers with extremely limited computational and memory resources. In this context, this work presents the design, implementation, and validation of a real-time cardiac arrhythmia classification system based on a quantized one-dimensional convolutional neural network (1D-CNN), deployed on an 8-bit Arduino UNO microcontroller. The proposed system integrates end-to-end processing, including ECG signal acquisition using a low-cost AD8232 analog front-end, signal preprocessing, heartbeat segmentation, classification, and real-time visualization on an OLED display. The model was trained and evaluated using the MIT-BIH Arrhythmia Database, considering a reduced three-class problem (Normal, Ventricular, and Supraventricular) to meet the constraints of ultra-low-cost hardware deployment. Under benchmark conditions, the quantized model achieved an accuracy of 97.6%, with a memory footprint below 24 KB and an average inference time of 200 ms per heartbeat, enabling real-time operation on a resource-constrained microcontroller. Real-time experiments were conducted using signals acquired from healthy volunteers to validate system functionality, although no annotated ground truth was available for these recordings, and therefore no diagnostic performance was derived from them. The results demonstrate the feasibility of deploying lightweight deep learning models on ultra-constrained embedded systems using the TinyML paradigm, implemented using TensorFlow 2.15 and TensorFlow Lite. This work should be interpreted as a proof-of-concept platform that highlights the trade-off between classification performance and hardware limitations, providing a foundation for future development of low-cost cardiac monitoring technologies in resource-limited environments.

Environmental monitoring across extensive regions is often constrained by the high costs of conventional laboratory analysis. This study proposes a methodology for electrophysiological characterization of Aloe vera as a potential biological dosimeter for low-cost environmental sensing. Using an ATMega328P-based acquisition system with high-input-impedance signal conditioning, we recorded plant biopotentials under controlled abiotic stressors. Signal variations were evaluated as a function of leaf morphology, electrode placement, and environmental variables, including light intensity, soil moisture, water saturation, and pH. The statistical validation included Jaccard similarity coefficients for repeatability and Kruskal–Wallis tests for group comparisons. The measurements showed highly repeatable baseline behavior (Jaccard similarity in the range 0.95–0.99) and significant differences across stress conditions, particularly under changes in light intensity. These findings support the feasibility of using Aloe vera electrophysiological signals as a robust and low-cost basis for developing plant-based biosensing approaches in environmental monitoring applications.

Water planning in semi-arid regions depends on climate information that resolves both seasonal timing and topographic gradients. This study evaluated 15 CMIP6 models over Zacatecas, Mexico, and produced a 1 km historical dataset for 1985–2014 by statistically refining bias-corrected daily fields from NEX-GDDP-CMIP6. Downscaling was referenced to the CHELSA climatology: temperature was refined using an elevation-informed hybrid spline approach, whereas rainfall was downscaled with geographically weighted regression (GWR) to represent orographic gradients. The resulting fields were assessed against two independent observational baselines: an automated INIFAP network (2004–2014) and a conventional CONAGUA network (1985–2014). For temperature, BCC-CSM2-MR showed the highest performance, with a Pearson correlation of R = 0.94 for both T_max and T_min. A consistent network-dependent bias pattern was identified: the downscaled models overestimated the diurnal temperature range relative to INIFAP but underestimated it relative to CONAGUA, highlighting the influence of instrumentation and observational protocols on model evaluation. For rainfall, ACCESS-ESM1-5 reproduced the seasonal cycle and dominant orographic patterns, with a correlation of R = 0.611 despite the intrinsic stochasticity of semi-arid rainfall. The resulting 1 km fields provide a spatially consistent baseline for regional applications, including stochastic weather generation and impact models in complex semi-arid regions.

Mineral scale deposits in groundwater-supplied drinking water distribution systems reduce hydraulic efficiency and increase maintenance costs. This study evaluated the effectiveness of citric acid as a descaling agent for natural scale deposits collected from the Benito Juárez and La Joya drinking water distribution systems in Zacatecas, Mexico. The samples were treated with citric acid solutions at concentrations of 1, 2, and 3 M (moles per liter) and characterized before and after treatment using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and inductively coupled plasma optical emission spectrometry (ICP-OES). In addition, statistical analyses were performed using analysis of variance (ANOVA) and Tukey’s test. The scale deposits were composed mainly of calcium carbonate and exhibited differences in their initial mineralogical composition between the two systems. Treatment with citric acid promoted the progressive dissolution of carbonate phases, resulting in mineralogical, chemical, and morphological changes. The 3 M solution produced the highest degree of mineral dissolution, whereas the 1 M and 2 M treatments showed statistically similar behavior. The results demonstrate that scale deposit removal efficiency is influenced by the initial composition of the scale deposits, providing a scientific basis for optimizing remediation strategies in drinking water distribution systems.