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This project details the implementation of an artificial neural network (ANN) as the principal element of a system engineered to identify and predict future control states of a thrust-propelled seesaw in an open-loop configuration. The primary objective was to maintain the seesaw in a balanced 90° position. The system’s dynamic behaviour was analysed under minimal external disturbances, facilitating development and evaluation in a controlled environment. Experimental data were captured via a programmable Arduino UNO board transmitting over the serial port and recorded in an Excel file for subsequent processing. A Kalman filter was applied to refine the data, from which a random subset was selected to train the neural network. A comprehensive analysis of the results is presented herein, demonstrating the ANN’s satisfactory performance in the control task.

An automatic temperature control system is an important application used in almost all modern gadgets, smart homes and industries; however, there are almost no available prototypes to practise with, and the ones that do exist cost more than an average student in Mexico can afford. This project is based on the implementation of a digital PID controller, realised through widely studied mathematical concepts, applied to a fan that blows air into an enclosed area housing an incandescent bulb, thereby controlling the internal temperature. A Kalman filter was used to reduce the thermocouple noise. The performance of the controller was tested by observing its ability to maintain the desired temperature setpoint steadily. This test highlighted the PID controller’s ability to reach and maintain the desired temperature, validating its effectiveness as both an educational tool and a method of control for industrial applications.

Nowadays, the recycling of metals from electrical and electronic waste is of great relevance due to its direct and indirect impact on environmental, social, and economic fields. Therefore, this study, conducted at the laboratory level, focuses on the recovery of copper from printed circuit boards through dynamic acid leaching in an H 2 SO 4 -O 2 system, with the stirring rate controlled as the main parameter. Initially, the metallic pins were characterized by SEM-EDS, revealing that they consist of 7.56 wt% of copper, the predominant element serving as the base material. A thin gold film (79 wt%) is deposited on the copper to enhance its electrical conduction properties. In the subsequent leaching step, a random sample of 10 g was taken in a 500 mL volume, with an acid concentration of 0.03 M. The system was heated to 298.15 K under an oxygen partial pressure of 101.3 kPa. The stirring rate was varied from 450 to 1000 rpm, resulting in a maximum copper concentration of 645.294 ppm in the solution. The experimental constants were calculated for low (0–60 min) and high (60–240 min) chemical attack times, yielding ranges of 0.026 to 0.923 and 0.019 to 2.577 min − 1 , respectively. On the other hand, one of the main outcomes of this research lies in the implementation of an artificial neural network to intelligently model the experimental process. It exhibited a mean squared error, correlation coefficient, and determination coefficient of 0.99690. Artificial neural networks emerge as an exceptional tool in predicting hydrometallurgical processes. This innovative application not only optimizes copper recovery but also ensures a cost-effective and environmentally friendly management of electronic waste. In the same way, it is possible to generate models of problems through learning. For all the aforementioned reasons, in the present work, an artificial neural network is developed to predict the dissolution of Cu in an electronic waste leaching process, considering the stirring rate as a key factor. Highlights • Electronic waste as secondary source of precious and non-precious metals. • Copper is obtained from recycled computer printed circuit boards through an environmentally friendly chemical process. • Artificial neural networks enable design and fault finding in complex systems by validating, aggregating and analyzing data. • Application of an artificial neural network for processing, identification and modeling of dynamic acid leaching systems. • A novel combined hydrometallurgical and artificial neural network process for the recovery of copper from electronic waste. Graphical Abstract

In this project, we present the design, construction, and control of a pendulum-like system propelled by a propeller mounted on a brushless motor. Programming and data acquisition were performed using an Arduino UNO board connected to a computer through the Arduino Integrated Development Environment (IDE). The measurement of the pendulum's angular position was conducted using a variable resistor. However, a drawback of this approach is its susceptibility to introducing noise into the signals. To address this issue, a Kalman filter was implemented in the analysis. The system was mathematically modeled as a second-order transfer function with underdamped poles and identified using the adaptive Gauss-Newton method. Experimental tests were conducted with step response trials, employing pulse width modulation (PWM) as the input and the resistor voltage as the output variable. The pendulum position was controlled using a Linear Quadratic Regulator with Gravity Compensation (LQR+G) and a Proportional-Integral-Derivative (PID) controller. Finally, a comparative analysis of performance was carried out between both approaches.

This article examines the importance of teaching electronics and process control within biotechnology engineering programmes, particularly in the context of Industry 4.0. With the increasing adoption of automation and the Internet of Things (IoT), it has become important to equip students with practical and applied skills. However, economic constraints and limited access to specialised software in many developing countries present significant challenges. This study illustrates how the use of affordable microcontrollers, such as the ESP32, together with open-source software, can provide an effective alternative that enables hands-on learning without compromising educational quality. In particular, the ESP32 incorporates Wi-Fi connectivity, which allows online control and monitoring, as well as data storage in external databases. Practical projects involving data acquisition, signal conditioning, and actuator control are intended to offer students experience with real-world applications. In addition, project-based assessment supported by detailed rubrics may enhance students’ understanding and performance, thereby preparing them more effectively for the demands of the contemporary workforce.   Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1200 Dimensions.Open Alex.

This research aims to apply architectural photogrammetry for the documentation and conservation of the Monumental Clock of Pachuca de Soto, in the State of Hidalgo. To this end, an unmanned aerial vehicle (UAV) of the quadcopter type was constructed, enabling the capture of high-resolution photographic images, which were processed to generate a detailed three-dimensional model of the structure. The results demonstrate that this technique provides an accurate representation of the monument, facilitating both its conservation and the planning of future interventions. This study highlights the importance of model quality, which is influenced by climatic variables, as image capture may be limited on cloudy or rainy days. The research emphasizes the innovative application of UAV-based photogrammetry in the field of architectural heritage preservation, offering an efficient and non-invasive tool for the documentation of historical monuments. The findings confirm that this methodology holds significant potential for implementation in the digital conservation of cultural heritage.

This work presents the design, construction, and validation of a cost-effective aerodynamic pendulum as an alternative to commercial systems. The system employs a digital PID controller for real-time regulation of angular position, together with a Kalman filter to attenuate sensor noise. Its modular architecture is intended to facilitate the integration of hardware and software components, thereby supporting replication and experimentation without compromising measurement accuracy. The prototype incorporates low-cost components, including an Arduino Mega 2560, a DC motor, a precision potentiometer, and an L298N H-bridge. A closed-loop configuration based on PID control is used to reduce the error with respect to a 90° reference angle, while the Kalman filter contributes to improved reliability of angular measurements. The mechanical structure, constructed from plywood and plastic materials, provides adequate stability for experimental operation. Motor voltage is regulated through a pulse-width modulation (PWM) signal. The results indicate a rapid dynamic response and a degree of robustness to external disturbances. Overall, the project supports the feasibility of developing effective low-cost alternatives for control systems, particularly in educational and experimental contexts.   Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1137 Dimensions.Open Alex.

Temperature control systems have various applications, from cooling to casting, and are crucial for ensuring quality in production. Although essential, their usage entails a significant energy consumption. This project focuses on implementing optimal control synthesized from the calculus of variations applied to the Hamilton-Jacobi-Bellman equation to regulate temperature within a finite volume space. The objective is to enhance thermal efficiency without compromising product quality. The approach not only aims to optimize energy consumption but also to ensure uniformity and quality in products and processes affected by temperature. This can be achieved by maintaining thermal stability at desired values and responsible resource management. In general, the article proposes improving efficiency and quality in temperature regulation, contributing to sustainable and effective industrial practices.

This article presents an alternative automation algorithm for pneumatic sequences that enables the solution of complex sequential problems without the need to organize detection phases into groups. By not using groups to organize movements in detection phases, two benefits can be achieved: firstly, all the drawbacks associated with using groups are eliminated, such as design complexity, difficulty in debugging, increased cost, and difficulty in optimizing the circuit; secondly, it enables the use of logical functions to define each motion in pneumatic actuators. This opens up the possibility to solve logical functions using Boolean algebra techniques, and even their generalization into an algorithm that can be programmed in any language. To validate the proposed algorithm, MATLAB pseudocode is presented. This pseudocode allows obtaining the logical functions needed to build the ladder diagram of any sequence involving up to four double-acting cylinders with any number of phases in a work cycle.