This review explores the efficiency and mechanisms of GQDs in removing contaminants from water, emphasizing their potential for environmental applications. GQDs possess unique physicochemical properties, such as a high surface area, tunable photoluminescence, and strong adsorption capacities, which enable the effective removal of diverse contaminants, including heavy metals, organic compounds, and dyes. Their electron-hole separation efficiency and functionalizability enhance their reactivity and selectivity. Notable findings include the integration of GQDs into advanced nanocomposites and supramolecular networks, significantly improving their adsorption and catalytic performance. However, challenges such as variability in synthesis methods, stability under environmental conditions, and the environmental impact of GQDs remain. Addressing these limitations and understanding the interaction mechanisms between GQDs and contaminants are critical. Future research should prioritize scalable green synthesis techniques, long-term environmental assessments, and optimized functionalization strategies to establish GQDs as a sustainable solution in water purification technologies.

The development and construction of highway infrastructure are essential in developing countries, whereas its layout and construction sometimes interact with the coastal environment. One problem to attend to is that the outline and geometry designs impact as little as possible on the hydrodynamic circulation of coastal bodies in order to alter the associated ecosystem as little as possible. The study area is located in the north of Colombia, and is made up of a continental coastal zone (Mallorquín Lagoon) and a marine zone (Caribbean Sea), in which a highway is projected that provides communication between two locations. This study presents the application of a numerical model previously developed and modified by the Berkhoff equation, which is developed in a finite difference scheme and has been validated and applied in different works in coastal and fluvial shallow water areas. The application of the model was carried out in a hydrodynamic circulation research project for a one-way highway through a coastal lagoon, where the knowledge of the magnitude of the incident wave height in the structure of the road body is necessary for the design, protection elements, and road geometry. Two numerical simulation scenarios were carried out, specifying normal conditions and extraordinary wave conditions in the month of November with a simulation time of 15 days, obtaining the velocity field associated with coastal currents, waves, and wave modification phenomena, such as refraction, diffraction, and reflection, which provide the height of the incident wave on the highway and the recirculation patterns in the coastal lagoon to identify alterations in the ecosystem. The results of the wave height in each scenario and the velocity field provide values to be used in the design, type of armor, and dimensions of the protection works required for the proper functioning of the road structure.

Maize pests and diseases significantly reduce crop yields and cause substantial economic losses. Manual monitoring is slow and difficult to scale, highlighting the need for automated diagnostic systems. This study presents a specialized two-stage stacking ensemble model for the automated detection of ten classes of maize pests and diseases from digital leaf images. The proposed method integrates optimized base models derived from seven transfer learning architectures under both feature extraction and fine-tuning schemes, combined through a stacking ensemble strategy optimized via Bayesian optimization. All possible combinations of base models were systematically evaluated to identify the most effective ensemble configuration. Furthermore, a specialized two-stage stacking model was implemented to enhance discrimination between visually similar classes, particularly between plants infested with fall armyworm and healthy plants. The final model achieved outstanding accuracy, surpassing previously reported results, demonstrating the effectiveness of the two-stage design and the optimized stacking ensemble approach. These findings indicate that integrating transfer learning models within a specialized stacking ensemble provides a robust, accurate, and scalable solution for the automated detection of maize pests and diseases.

Este artículo describe el diseño e implementación de un medidor caudal inteligente basado en tecnologíasdel internet de las cosas (IOT). El sistema incorpora un caudalímetro para medir el flujo de agua y una válvulade bola que se acciona mediante un servomotor, acoplado mecánicamente a la válvula. El prototipo funcionaen dos modos de operación. En el modo local, se controla la válvula con un potenciómetro y se visualiza elcaudal en una pantalla OLED. En el modo remoto, aplicando protocolos de comunicación de internet de lascosas (IoT), se puede controlar la válvula y monitorear el flujo a través de una interfaz gráfica creada con elsoftware Node-RED. Esta interfaz permite al usuario abrir o cerrar válvulas y monitorear el caudal de formaremota. El microcontrolador ESP32 es el núcleo del prototipo realizando tareas de monitoreo, control,conexión a internet y la implementación de protocolos MQTT (Message Queuing Telemetry Transport). Elartículo detalla los elementos principales del sistema implementado, los algoritmos utilizados para elfuncionamiento del prototipo, así como los resultados y pruebas que validan su efectividad y versatilidad.