One of the environmental challenges that is currently negatively affecting the ecosystem is the continuous discharge of untreated industrial waste into both water sources and soils. For this reason, one of the objectives of this qualitative study of exploratory-descriptive scope was the review of scientific articles in different databases—Scopus, Web of Science, and Science Direct—published from 2010 to 2021 on the use of fruit peels as a sustainable waste in the removal of heavy metals present in industrial wastewater. For the selection of articles, the authors used the PRISMA guide as a basis, with which 210 publications were found and 93 were compiled. Considering the reported work, a content analysis was carried out using NVivo 12 Plus and VOSviewer 1.6.17 software. The results show that the fruits mentioned in these publications are lemon, banana, mango, tree tomato, pineapple, passion fruit, orange, coconut, avocado, apple, lulo, and tangerine. However, no studies were found with lulo and tree tomato peels. On the other hand, the heavy metals removed with the selected fruit peels were Pb⁺², Cr⁺³, Cr⁺⁶, Ni⁺², Cd⁺², As⁺⁵, Cu⁺², and Zn⁺².

This paper presents an in-depth look at the evolution of Vehicular Ad Hoc Networks (VANET) and their increasing importance in the development of Intelligent Transportation Systems (ITS). Emerging technologies that shape the future of VANET are discussed, focusing on artificial intelligence (AI) and software-defined networking (SDN) as key solutions to the challenges inherent in these dynamic networks. Applications of AI, including machine learning (ML), deep learning, and swarm intelligence, for improving security, traffic efficiency, and information management in VANET were examined. Routing challenges in these environments are discussed and SDN is presented as a viable solution. SDN protocols, controllers, and architectures are described, and their advantages and disadvantages in the context of VANET are analyzed. It provides a comprehensive overview of the current state of VANET, the emerging technologies driving them, and the challenges and opportunities presented by their continued development. The convergence of AI, SDN, and other key technologies is crucial for intelligent transportation, paving the way for safer, more efficient, and more connected driving.

The present research shows the results obtained from the biosorption process of Pb, using coffee pulp as a biosorbent in synthetic waters. To do this, the lignin and cellulose content and the percentage of removal of Pb²⁺ ions was determined; additionally, the sorption’s optimal variables, such as the optimum pH, the point of zero charge (pHpzc), the kinetics and the adsorption isotherm, were determined. A comparison was made with other by-products derived from coffee crops. According to the results obtained in this research, the cellulose percentage was 29.12 ± 0.22% and the lignin percentage was 19.25 ± 0.16% in the coffee pulp, the optimum pH was 2.0 units and the kinetic model, which adjusted to the biosorption’s process, was the pseudo-second order of Ho and McKay, presenting an isotherm of Langmuir’s model and pHpzc of 3.95 units. Lastly, the removal of the pollutant was 86.45%, with a capacity of maximum adsorption of 24.10 mg·g⁻¹ obtained with a particle size of 180 µm, time of contact of 105 min and at 100 RPM. Finally, we express that (a) the coffee pulp can be used as a sustainable alternative for the removal of the pollutant mentioned in synthetic and/or industrial wastewater matrices, to meet goals 3.9 and 6.9 of the Sustainable Development Goals of the 2030 agenda, and (b) the novelty of this research is the use of an agricultural waste of easy acquisition as a sorbent, without chemical modification, since it presented a high percentage of efficiency in the removal of Pb²⁺ ions. In turn, the challenge of this research is implementing this green technology on a pilot, semi-industrial and/or industrial scale in wastewater treatment systems.

This manuscript describes a swift control prototyping (SCP) approach implemented within an industrial setting, utilizing a DC-DC nonlinear quadratic buck converter (QBC) to decrease production expenses of electronic materials and devices while improving power system efficiency. A practical experimental setup is utilized to develop a working prototype, transforming 380 VDC to 48 VDC with a power capacity of up to 500 W. The setup incorporates dSPACE CP1103 to execute Model in the Loop (MIL), Software in the Loop (SIL), and Hardware in the Loop (HIL) methodologies. Additionally, it employs advanced control strategies such as passivity-based control (PCB) and canonical forms, which are compared with classical Proportional-Integral-Derivative (PID) control arrangements to propose a comprehensive control scheme that ensures stability in the presence of real-time disturbances (RT). Finally, a performance analysis is conducted, assessing important indicators like reaction time, precision of signals, stability of the system, and efficiency in resource utilization. The document concludes by offering a performance evaluation that encompasses PI, Cp, CpK, Z-score, and ITAE. This assessment considers factors such as response time, signal accuracy, system stability, and the efficiency of resource utilization.