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The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R 2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

The crystallographic, optical, and electrical properties of manganese sulfide thin films depend on the control of the temperature precursors in the synthesis process, as shown by the results of this work. MnS thin films were deposited on glass substrates using the SILAR method and over an additional layer of CdS synthesized by chemical bath deposition (CBD) to acquire a p-n heterojunction. SILAR is an inexpensive method performed with a homemade robot in this case. Temperature in the solution precursors varied from 20 to 80 °C in four experiments. The morphology and structure of MnS and FTO/CdS/MnS thin films were studied through scanning electron microscopy (SEM) and grazing-incidence X-ray diffraction (GIXRD); the results indicate that materials showed a polycrystalline behavior, a diffraction peak of α - MnS cubic phase was observed with lattice constants values, ranging from 4.74 to 4.75 Å. Additionally, Raman spectroscopy showed a signal corresponding to the transversal optical phonons of MnS at a wavenumber near 300 cm -1 . UV-vis spectroscopy showed optical bandgap values of 3.94, 4.0, 4.09, and 4.26 eV for thin films obtained at 20°, 40°, 60°, and 80 °C. respectively. Results indicated 80 °C as an optimal cationic precursor process temperature, achieving optical transmittance T% and good film quality according to SEM and GIXRD for the synthetization of MnS. The current-voltage (I-V) characterization in the heterojunction showed a characteristic diode curve with an open circuit voltage (VOC) of 300 mV under illumination, which indicated that the manganese sulfide behaves as p-type material contributing with positive charge carriers, while CdS behaves as n-type material., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.