Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

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.

The world has changed drastically in logistical and economic spheres as a result of the COVID-19 pandemic. This pandemic has caused a global crisis in supply-chain structures, creating regional, national and international impacts of unprecedented magnitude. Accordingly, this research develops a methodology to favour the logistics-resilience framework based on regional externalities and technical-efficiency analysis of the 51 US states, applying a Spatial Data Panel model and a Stochastic Frontier model in conjunction with graph theory (Ford–Fulkerson algorithm). The findings indicate that New York, West Virginia and North Dakota are vital external regions to support California’s logistics resilience. We demonstrate that a region with high technical efficiency does not necessarily constitute a key logistics spillover for a target region. This study represents one of the first attempts to optimise and redirect externalities from one region to another using spatial and logistical mechanisms.

The autonomous construction of environment maps with the help of mobile robots is an important problem in modern robotics; because practically all tasks performed by robots require a representation of the working environment. Many solutions have been proposed to solve this problem known as SLAM (Simultaneous Localization and Mapping). The inclusion of a motion planner to the classical SLAM problem gives way to a new approach known as “Integrated Exploration”, in which a robot gradually builds a map while simultaneously localizing itself and making local decisions on where to go to maximize the acquisition of map information. In this paper we will analyze the proposals that have been developed in the last 20 years in this area, having as a primary interest to show the advances that have been made in the area of motion planning and the challenges presented by its integration and coordination with the SLAM problem.

This paper examines the role of Artificial Intelligence in enhancing human well-being across domains such as healthcare, mental health, and education. A key contribution is the introduction of synthetic happiness—a form of well-being facilitated or enhanced by AI rather than naturally occurring processes. By reviewing current advancements, the study highlights AI’s positive impact on life satisfaction while addressing ethical concerns and potential drawbacks. The paper explores AI’s role in personalised healthcare, mental health support, and adaptive education, demonstrating how it fosters environments conducive to happiness. It further analyses synthetic happiness as a novel perspective on AI-driven well-being, discussing its benefits and risks, including reduced human interaction and over-reliance on artificial systems. While AI presents transformative opportunities to enhance happiness, it must complement rather than replace genuine human experiences. This paper provides a foundation for understanding AI’s role in well-being and offers insights for future research and applications.

Este trabajo presenta un método basado en visión artificial y procesamiento de imágenes médicas para la detección automática del centroide del cóndilo femoral en artroplastias totales de rodilla (TKA). Se implementa el preprocesamiento de imágenes DICOM, seguido de la segmentación del cóndilo femoral, la detección de bordes mediante el operador Canny y el cálculo del centroide mediante la selección de cuatro puntos anatómicos clave. Esta técnica reduce la variabilidad en la detección manual, mejorando la alineación de los implantes protésicos y optimizando la planificación quirúrgica. Los resultados demuestran que la detección del centroide es precisa y reproducible, lo que permite la integración en sistemas de navegación quirúrgica y cirugía robótica. Este desarrollo representa un avance en inteligencia artificial (IA) para cirugía ortopédica, mejorando la precisión de los procedimientos y reduciendo los tiempos quirúrgicos.

The project involves the development of an intelligent system for managing and optimizing water consumption in residential settings, addressing the significant loss of this vital resource due to leaks in domestic pipelines, particularly in toilets. To mitigate the issue of silent water leaks in toilets, a mechanism based on Hall effect sensors was implemented, allowing real-time monitoring of water flow in the toilet tank, detection of irregularities, and user notification through a real-time communication network. The designed system integrates a PIC18F4550 microcontroller, YF-B10 flow sensors, and a solenoid valve, all interconnected to a cloud database and linked to a graphical interface accessible from mobile devices. To evaluate its performance, water flow tests were conducted, obtaining data on the relationship between the pulses generated by the sensor and the actual flow rate. Through machine learning models, anomalous consumption patterns were identified, establishing a reliable method for early leak detection. Preliminary results demonstrate that the system can significantly reduce water wastage by promptly alerting users to potential leaks, thereby contributing to water conservation and environmental sustainability.

Information technology (IT) governance and management are critical to organizational success inthe digital age. The COBIT (Control Objectives for Information and Related Technologies) frameworkhas been widely recognized as an effective tool for aligning IT with strategic objectives, managing risksand ensuring regulatory compliance. However, COBIT implementation and monitoring can becomplex and resource-demanding, which has generated growing interest in its automation. Thisarticle explores how COBIT model automation, through the integration of technology, can optimizeIT management processes, improve the accuracy of controls, and provide real-time information.Additionally, the challenges inherent to this automation are addressed, such as adaptation tospecific contexts and integration with pre-existing systems. Based on a theoretical analysis and a casestudy, a reference framework is offered for the successful implementation of COBIT automation,highligh its potential to transform IT governance in organizations in various sectors

Energy management in smart buildings requires strategies to improve power consumption, ensuring reliability and cost reduction. This thesis addresses the solution of the problem by implementing a predictive model based on an artificial intelligence technique working in conjunction with a proposed battery bank in order to better store energy and mitigate the high peaks of electricity demand in different periods as well as in atypical scenarios.   The research addresses two aspects: accuracy in consumption prediction, and energy management, the latter including energy storage efficiency and mitigating high peak power consumption. The proposal aims for the expected results to reduce electricity consumption by 3% to 7%, validating the viability of this scalable and sustainable solution to improve energy efficiency in smart infrastructures.

Precision agriculture has revolutionised the way farmers manage their crops, maximising efficiency and minimising environmental impact. This thesis addresses the design and implementation of a device that identifies the corn plant and with the help of artificial intelligence implement a robotic arm that delivers herbicides and in some cases precision lasers.   We will explore the design and control of a precision farming device, from basic principles to the most advanced technologies currently in use. We will analyse the sensorisation tools, control systems and optimisation techniques employed to ensure a more sustainable and profitable agriculture through: precision identification of maize plants and the use of a robotic mechanical arm equipped with two nozzles, one to deliver herbicide and the other to deliver lasers to the weeds surrounding the maize plants. The proposal aims for the expected results to reduce herbicide consumption by 30-50%, validating the feasibility of this scalable and sustainable solution to improve efficiency in agricultural infrastructures.    

Maritime technology has evolved significantly since the 16th century when navigation relied on sails and tools such as the astrolabe and sextant. In the 19th century, the Industrial Revolution introduced steam propulsion and iron structures, improving the strength and efficiency of ships. In the 20th century, combustion engines, radars, and GPS revolutionized maritime safety and navigation. In the 21st century, artificial intelligence has optimized traffic management, predictive maintenance, and autonomous navigation, reduced costs, and improved safety. This article reviews the technological advances in maritime technology and its evolution towards a sustainable future.