Photothermal techniques allow the detection of characteristics of material without invading it. Researchers have developed hardware for some specific Phase and Amplitude detection (Lock-In Function) applications, eliminating space and unnecessary electronic functions, among others. This work shows the development of a Digital Lock-In Amplifier based on a Field Programmable Gate Array (FPGA) for low-frequency applications. This system allows selecting and generating the appropriated frequency depending on the kind of experiment or material studied. The results show good frequency stability in the order of 1.0 × 10⁻⁹ Hz, which is considered good linearity and repeatability response for the most common Laboratory Amplitude and Phase Shift detection devices, with a low error and standard deviation.

This article proposes the creation of a course based on a series of practical sessions, where the students have to develop their practical knowledge about artificial intelligence techniques, specifically multilayer perceptron. The novelty of this paper is based on the constructivism methodology regarding artificial intelligence and sustainable development. Moreover, it can be implemented in different majors because of the flexibility in certain aspects. It is oriented to evaluate skills in the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. The proposal helps the students to turn theoretical concepts into more tangible objects where they can build their knowledge by programming their implementations in software. Then, programming codes for practicing the neural networks theory, finite impulse response, empirical mode decomposition and discrete wavelet transform are achieved to compare percentage classification between different techniques. Also, it measures the interaction between the student and the theoretical mathematics of artificial intelligence. The continuous evaluations at the end of the practical sessions corroborate the increase in the knowledge of the students. A study based on rubrics illustrates an increase in the average grade obtained by the students in the elaboration of each practice. Finally, a senior project is carried out by taking into account sustainable development issues and the usage of tools of artificial intelligence.

The use of stationary bikes has increased significantly, making physical activity more accessible to the general population. However, the lack of precise, non-invasive monitoring during training sessions can mask muscle imbalances and inefficient biomechanical patterns. These factors, subclinical in their initial stages, can lead to a high incidence of musculoskeletal injuries. Currently, exercise evaluation relies on the subjective perception of effort or conventional performance metrics, creating a critical gap in the early detection of physiological issues before they progress to chronic conditions. Given the crucial role of exercise in public health, it is imperative to implement sustainable evaluation strategies applicable to various fitness levels. In this context, infrared thermography (IRT) emerges as a promising alternative due to its ability to quantify skin surface temperature, a direct indicator of metabolic activity, thermoregulation, and blood flow. This project establishes a standardized methodological protocol using IRT to monitor the physiological response during cycling sessions in a non-athletic population. The application of this protocol revealed that standardized IRT can effectively detect subclinical thermal asymmetries and localized "hotspots" in joints (e.g., the knee at 32.8 °C) that are otherwise undetectable through subjective effort perception (Borg scale) or pedaling cadence monitoring. By systematically controlling environmental and subject-related variables, this research analyzes surface temperature distribution in the lower body to evaluate biomechanics and muscle fatigue. Specifically, the protocol facilitates the correlation between thermal patterns, fatigue levels, and pedaling cadence. This approach not only supports technological innovation in preventive medicine and public health but also lays the groundwork for an early detection tool capable of mitigating injury risks and optimizing the well-being of the population engaged in this activity.

Cognitive stress detection based on electrocardiogram (ECG) is challenged by the high dimensionality of multichannel analysis, redundancy between heart rate variability (HRV) and morphological descriptors, and variability in classifier performance. We developed and evaluated a cognitive stress classification framework based on a standardized ECG acquisition protocol, the integration of HRV and morphological descriptors extracted from 12 leads, and an adaptive feature selection strategy using a binary genetic algorithm with explicit penalization of dimensionality. Seventy healthy students aged 18–25 years participated, and cognitive stress was induced using a task based on PMA-R Factor R. The initial dataset included 27 descriptors per lead, and the proposed dimensionality reduction method was compared with two reference schemes: no dimensionality reduction and conventional principal component analysis (PCA) with a 99% cumulative explained variance threshold. Performance was assessed over 30 data splits using five classifiers: logistic regression, linear support vector machine (SVM), radial basis function SVM (SVM-RBF), k-nearest neighbors (KNN), and decision tree. The best trade-off between parsimony and predictive performance was achieved with $\lambda =0.05$, yielding a compact subset of 11 features on average and a mean AUC of 0.830. In the final comparison, the adaptive strategy achieved the best overall performance with SVM-RBF (AUC $=0.830\pm 0.047$; specificity $=0.814\pm 0.115$), outperforming both the full feature set and PCA. These findings indicate that penalized genetic selection validated across multiple classifiers is an effective strategy for identifying compact, discriminative, and robust feature subsets for ECG-based cognitive stress classification.

Increased water scarcity represents a significant threat to global lettuce production, reducing crop quality and yield. While producers often resort to overfertilization to mitigate water stress, biological alternatives such as microbial inoculants offer a more sustainable approach. This study evaluated the effectiveness of a 67-strain microbial consortium and a Trichoderma harzianum isolate in enhancing water stress tolerance in Romaine lettuce (Lactuca sativa var. Paspartu) under controlled conditions. Nine treatments were established, combining microbial inoculation with three irrigation levels (50%, 75%, and 100% of evapotranspiration). Crop water status was monitored using infrared thermography to calculate the Crop Water Stress Index (CWSI) from fluctuations in canopy temperature (Tc). The statistical analysis (ANOVA) of the CWSI showed no globally significant differences between treatments (F = 0.67, p = 0.778); however, distinct numerical trends were observed during peak thermal stress events. Under severe water deficit (50% irrigation), plants inoculated with the microbial consortium maintained lower CWSI values (averaging 0.38) compared to non-inoculated controls, which reached maximum stress thresholds (CWSI = 1.0). Furthermore, microbial treatments demonstrated a faster thermal recovery following acute stress periods. These results suggest that while global variance is high, microbial consortia function as physiological buffers that enhance transpiration efficiency. Infrared thermography proved to be a precise, non-destructive tool for early detection of water stress. This study highlights the potential of beneficial microorganisms to improve crop resilience and provides a standardized protocol for thermal evaluation in horticultural research.