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Recommender systems -RS- have experienced exponential growth in various fields, especially in the tourism sector, improving tourism activities’ accuracy, personalization, and experience, thus strengthening indicators such as promotion. However, some challenges and opportunities exist to overcome, such as the lack of data on emerging destinations wishing to adopt these solutions. This manuscript presents a literature review of the current trends in RS applied to the tourism industry, including categories associated with their use and emerging techniques. Likewise, it presents a pathway for implementing an RS when insufficient data are available for a destination. The SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and used the WoS, Science Direct, and Scopus databases. The results show that the hybrid RS integrates deep learning algorithms, data analytics, and optimisation techniques with collaborative tourism features to provide innovative solutions in terms of performance, accuracy, and personalisation of recommendations, thus achieving the management of tourist destinations or tourism-oriented services. Emerging destinations that lack RS data in tourism should use various data sources generated by tourists on social media, tourism portals, and through their interaction with tour operators. New tourism recommender system solutions can emerge following trends integrating new technologies based on user experience, collaboration, and the integration of multiple data sources.

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

Circular Economy (CE) plays a crucial role in Latin America, where the transition to new economic development models poses significant challenges. This study conducts a bibliometric analysis of CE research in the region to identify critical areas of development, influential authors, organizations, and future research trends. This analysis aims to highlight the progress made in the CE field in Latin America and identify areas for improvement to promote sustainable development. Using the Scopus database, we analyzed 632 research papers, and the rising number of CE publications in Latin America suggests a continuous growth trajectory, expected to reach over 2500 studies by 2026. The analysis reveals that Brazil leads CE research with 274 publications, Waste Management being the most studied topic. The study highlights the region's growing trend towards implementing innovative and sustainable solutions for waste management and resource utilization, such as bioengineering and biochemistry processes, which could positively impact the region's economy and environment. The findings of this study provide valuable insights for policymakers and researchers aiming to promote sustainable development in Latin America.

During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors’ measurements during extreme weather events and normal precipitation events during 2015–2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015–2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument’s reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.

Existen diferentes lenguajes de programación de Controladores Lógicos Programables (PLC), el más común de ellos es el lenguaje de programación gráfico para autómatas programables llamado LADDER. Sin embargo, el lenguaje LADDER no es amigable en programas que requieren de cálculos de ecuaciones matemáticas, por ejemplo, el cálculo del volumen de un tanque en un control de nivel. Lenguajes alternativos como el Lenguaje de Control Estructurado (SCL) son menos estudiados dada su complejidad y poca popularidad en la pequeña y mediana industria. En este artículo se compara los resultados del lenguaje de programación LADDER y el lenguaje SCL generado mediante la herramienta Simulink® PLC Coder de MATLAB® tomando como caso de estudio el cálculo de volumen de un tanque. Finalmente, este trabajo demuestra que la herramienta Simulink® PLC Coder presenta una alternativa de desarrollo más eficiente.

Knowledge is a competitive advantage for companies. Knowledge Management helps to keep this competitiveness. Universities face with challenges in research, innovation and international competitiveness. The purpose of this paper includes studying Knowledge Management Models, and Innovation Models apply to Research Groups of Universities, through an analysis of relation in inter-organizational level. Some researchers and leaders of research groups participated in a survey about knowledge management and innovation. Here we show the relationship between knowledge management, innovation and research, including processes and operations performed by universities around these. We organize the results in three dimensions: Knowledge Management perception, the relationship between Knowledge Management and Innovation, and Strategic Knowledge organization. Too, we identify a generality of good practices, challenges, and limitations on Research Groups for Knowledge Management.

El artículo tiene como propósito presentar los fundamentos conceptuales y los resultados preliminares de la investigación científica tecnológica sobre Desarrollo de un Laboratorio Remoto (LR), donde se esbozan las principales características que deben poseer los laboratorios remotos y las variables que se deben tener en cuenta durante la creación de un proyecto de teleoperación con el fin de convertirlo en un verdadero escenario de prácticas para los estudiantes de Ingeniería. La práctica para la ingeniería se constituye como una prioridad en la formación de los ingenieros, si se tiene en cuenta que el campo ocupacional les exige contar con las competencias necesarias para resolver problemas reales donde el componente práctico juega un papel preponderante. En la actualidad las instituciones educativas cuentan con limitados equipos y laboratorios para las prácticas de sus estudiantes, teniendo que recurrir a los laboratorios remotos como alternativa para suplir esta necesidad, ya que esta experimentación se puede llevar a cabo de manera remota a través de las Tecnologías de la Información y la Comunicaciones, TIC, sin restricción de tiempo y espacio. Es por ello que los LR han empezado a coexistir con los laboratorios tradicionales en el ámbito de la educación superior.