Worldwide, the shortage of fresh water has increased exponentially due to population growth and contamination of available water, especially in water tables that provide water for general consumption. One of the main pollutants of water is arsenic (As), present in the environment and in most mining/metallurgical processes, which is a major health risk, especially as a carcinogen. In the region of Matehuala, San Luis Potosi (SLP), Mexico, a highly productive mining area, arsenic concentrations of 138.1 mg/kg have been found in soils—6.2 times higher than what is allowed in domestic soils, while in water it is reported up to 158 mg/L, exceeding permissible limits for human consumption. In addition to As pollution, the region suffers from water shortage both in the city and in rural communities. Therefore, it is necessary to explore new technologies to provide the population with fresh water. This paper presents a feasibility study on the use of an atmospheric water generator (AWG) to capture fresh water in the region of Matehuala, SLP. The region was found to have the necessary environmental conditions to use AWGs, with an annual average relative humidity (RH) of approximately 60%. Using a mathematical model of a dehumidifier, water harvesting can be evaluated under the region’s prevailing climatic conditions. The month with lowest harvest was found to be January, with 0.89 to 3.6 L/day, while the month with largest harvest was August at 3.9 to 18 L/day and water production costs of 0.0093 and $ 0.038 USD/L, respectively. The study concludes that the use of AWGs would help alleviate water shortages, thus benefiting marginalized people or communities, preserving ecosystems and the environment.

This work presents the design of a new breast prosthesis using the biomimetic technique for cases of complete mastectomy to address the problem of the increasing number of women diagnosed with breast cancer in Mexico who are candidates for a mastectomy. The designed prosthesis considers the morphology of a real breast regarding its internal structure to obtain authentic mobility and feel. In order to accomplish this, a model was obtained in 3D CAD using a coordinate measuring machine (CMM) that can be scalable without losing its qualities, and which can be used in any type of patient; afterwards, a finite element model was developed and a static analysis performed with suggested load cases to evaluate the sensitivity and naturalness of the prosthesis; and finally, a modal analysis was conducted. The results obtained in displacements and in distribution of stress for the load cases assessed are consistent with those of a real breast: there were smooth contours and there was natural mobility in the prosthesis designed by means of the biomimetic technique.

This chapter provides a comprehensive overview of the integration of artificial intelligence (AI) and related technologies in the manufacturing sector, detonating Smart Manufacturing. It discusses the pivotal role of AI in enhancing operational efficiency, optimizing production processes, and improving product quality through data-driven decision-making, including the IoT and Big Data integration. All these technologies are applied in. predictive maintenance, quality control, and supply chain optimization, showcasing real-world case studies and examples. Additionally, it addresses the challenges and opportunities associated with implementing and the interaction of. AI, IoT, Industry 4.0, and Big data in manufacturing, emphasizing the importance of fostering a culture of innovation and continuous improvement. The findings underscore the transformative potential of AI in driving the evolution of smart manufacturing, ultimately contributing to increased competitiveness in a rapidly changing global economy.

This work presents the development of a methodology that, through the use of the coordinate transformation method, identifies the ideal modal parameters that should be used during the modal balancing process as well as allows eliminating the computational modes generated during the rotor response diagram extraction process. There is currently a wide variety of methods for structures that allow extracting limited modal parameters, such as: previous knowledge of the number of modes, adjustment of computational or spurious modes, close mode identification problems, and others. However, localizing the phase angle in rotation systems in any angular position and through complex coupling of the vibration modes does not ensure that the methods developed for structures conserve the same performance during the adjustment process. As regards the line of investigation into modal balancing, a method is proposed that allows ensuring that the modes found are real modes of the system and that through direction tracking where a single vibration mode is excited, the optimum extraction position of the modal parameters used in the balancing process can be determined. The proposed methodology was developed using a linear model and was applied in a field turbogenerator to identify the vibration modes present in the response diagrams.

Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.