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.

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation.

This study reports the thermodynamic performance of a patented compact vertical evaporator–absorber unit for LiBr–H₂O absorption cooling, extending Part I by translating validated prototype data into a rigorous control-volume assessment of coupled transport. Coolant-side calorimetry was used to determine the absorption heat-transfer rate (Q_abs), while a mass–energy balance provided an estimate of the absorption mass-transfer rate (${∆\dot{m}}_{abs}$) across twelve manually imposed thermal-load phases with tagged fan-OFF/ON sub-intervals. Linear trend (slope) analysis was applied to quantify phase-resolved dynamic behavior. Fan assistance produced three load-dependent regimes: (i) stabilization of downward trends under low and zero loads, yielding slope-based relative improvements above 100% in the most critical weak-gradient phases; (ii) acceleration of recovery at intermediate loads; and (iii) moderation of strongly positive drifts at high loads. The global thermal resistance ($R_{th}$) decreased by more than 30% in passive and low-load phases, and Wilcoxon signed-rank tests confirmed statistically significant reductions in most intervals (p < 0.05). Uncertainty contributions and robustness were quantified through an uncertainty budget decomposition and sensitivity analyses, and a subsystem-level normalization (η_EA = Q_abs/Q_in) is reported to support comparisons across loads without invoking cycle COP. Overall, active vapor-flow management using a low-power internal fan widens the useful operating envelope of compact absorbers and provides a validated thermodynamic baseline with practical, regime-aware control guidelines for decentralized low-carbon cooling technologies.