Industry 4.0 in the Theme Park Sector: Design of a RealTime Monitoring System for Queue Management

The theme park industry is a consolidated sector where different industrial technologies and management procedures are present. However, the Industry 4.0 paradigm aims at disrupting how industrial processes are conceived. In this thesis, we perform a thorough investigation of key relevant features of theme parks and how industry 4.0 could be applied within the theme park sector. Our methodology is as follows. First, we analyse the technology used in the most innovative attractions. Afterwards, we focus on the most recurrent problem within the sector: queue management at attractions. As part of the solution, a system is designed to allow real-time monitoring of waiting times through an IoT infrastructure. Radio Fre- quency Identification and Bluetooth Low Energy are the chosen technologies for people counting. They allow users to be located in the park in addition to counting. This system gives precise waiting times estimates, and park managers can obtain precious data about user behaviour and preferences. Finally, we develop a proof of concept to test the designed solution and detail the benefits of applying industry 4.0 to the theme park sector.Máster en Industria Conectada 4.

Algoritmos de aprendizaje profundo para procesamiento de video en dispositivos Xilin Zynq UltraScale+ de bajo coste

Las redes neuronales han sido la revolución tecnológica de comienzos de este siglo, debido a su gran potencial anteriormente inalcanzable debido al alto nivel de computación requerido para ello. Es por eso que cada año aparece nueva documentación y estudios sobre redes neuronales, puesto que al aumentar la tecnología de procesado se aumenta la capacidad de implementar mejores redes. Aun así a veces no se busca la mejor o más potente solución de entrenamiento de redes más potente, sino una adaptada a cada caso de inferencia, por lo que se buscan opciones de bajo consumo, baja latencia o que puedan ir en sistemas Edge. Es ahí donde aparecen opciones como las FPGA para adaptarse a distintas necesidades, y con ellas Xilinx con el software de Vitis AI con su entorno de desarrollo para implementar redes neuronales sobre sus arquitecturas de hardware de forma más sencilla y dinámica. En este trabajo se parte de la necesidad de explorar la implementación de redes neuronales sobre FPGA para ver su capacidad de dar soluciones a aplicaciones reales, como, en el caso a desarrollar, de clasificación de imágenes mediante CNN. Primero se hace un repaso al estado actual del arte de las redes neuronales desde sus orígenes hasta la actualidad. A continuación, se explica la forma de implementar estos sistemas mediante el software de Vitis AI y las herramientas que proporciona respecto a un desarrollo clásico. Por ultimo se realiza un caso practico del que se parte de una descripción de una red neuronal en Python hasta su despliegue en la placa ZCU104, detallando el flujo de trabajo realizado y obteniendo resultados de rendimiento y precisión para valorar el uso de estas tecnologías como solución de inferencia para redes neuronales

Una aproximación dinámica a la medición del riesgo de mercado para los bancos comerciales en Colombia.

En este artículo se describe la metodología utilizada para la medición del riesgo de mercado llevada a cabo en el Reporte de Estabilidad Financiera, mediante el uso de técnicas dinámicas no sólo en la modelación de volatilidades sino también de correlaciones. La medida de Valor en Riesgo (VeR) se calculó individualmente para los bancos comerciales con periodicidad semanal entre febrero de 2003 y febrero de 2008. Los cálculos de los VeR estáticos y dinámicos muestran diferencias cuantitativas significativas en períodos de turbulencia, lo que resalta la importancia de las nuevas medidas de riesgo propuestas.

Sistema de administración y control financiero de la asistencia técnica no reembolsable del ministerio de planificación

Describe el sistema de administración y control financiero de la asistencia técnica no re-embolsable, coordinado por la Dirección ejecutiva de cooperación internacional (DECI), del ministerio de planificación (MIPLAN)que en el documento se fija de otra form

Aedes albopictus in a recently invaded area in Spain: effects of trap type, locality, and season on mosquito captures

Mosquitoes are primary vectors of pathogens impacting humans, wildlife, and livestock. Among them, the Asian tiger mosquito, Aedes albopictus, stands out as an invasive species with a global distribution, having established populations on every continent except Antarctica. Recent findings incriminate Ae. albopictus in the local transmission of several pathogens causing human diseases, including dengue, chikungunya, and Zika viruses and worm parasites as Dirofilaria. In Spain, the establishment of Ae. albopictus occurred in 2004 and it rapidly expanded, currently reaching southern provinces and creating novel epidemiological scenarios in recently invaded areas. In this study, we conducted captures of Ae. albopictus from May to November 2022 in two provinces, Granada and Malaga, situated near the current edge of the species' expanding range in Spain. The objective was to identify the primary factors influencing their captures in these regions. Mosquitoes were captured using BG-Sentinel traps baited with CO2 and BG-Lure, and miniature CDC-UV traps in five different localities. Our findings underscore the influence of both extrinsic factors, such as locality, and intrinsic factors, including mosquito sex, on the abundance of captured Ae. albopictus. A higher abundance of Ae. albopictus was observed in the Malaga province compared to localities in the Granada province. Furthermore, similar numbers of Ae. albopictus mosquitoes were captured in more urbanized areas of Granada, while the lowest counts were recorded in the less urbanized area. These results were compared to captures of another common species in the area, specifically Culex pipiens. Overall, these results represent the first monitoring of invasive Ae. albopictus in the area and are discussed in the light of the potential importance of the species as a nuisance for humans and vectors of pathogens of public health relevance.This study was financed by the PID2020-118205GB-I00 grant to JMP funded by MCIN/AEI/https://doi.org/10.13039/501100011033. Additional support derived from the CNS2022-135993 grant of the Ministerio de Ciencia e Innovación (MCIN/AEI/https://doi.org/10.13039/501100011033) with funding from European Union NextGenerationEU. Mario Garrido was supported by the María Zambrano program and the P9 program for the Incorporation of Young Doctors funded by Spanish Ministry of Universities, the European Union-NextGenerationEU, and the University of Granada. Jesús Veiga received financial support from the Margarita Salas and Juan de la Cierva (FJC2021-048057-I) programs funded by Spanish Ministry of Universities, the Spanish Ministry of Science and Innovation and the European Union-NextGenerationEU. Marta Garrigós was supported by a FPI grant (PRE2021-098544). Mario Garrido is currently granted by the PID2022-137746NA-I00 funded by Spanish Ministry of Science and Innovation. We greatly appreciate the support given by the “Grupo de Investigación Comportamiento y Ecología Animal” of the University of Granada for the field sampling

Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics

We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.Comment: Main manuscript: 23 pages including references, 20 pages text, 8 figures and supplementary information include