El paper dels sistemes naturals de tractament d'aigües residuals en la protecció dels recursos hídrics

L’aigua és un bé valuós i essencial, indispensable per a totes les formes de vida. Aquest fet, conjuntament amb la seva fragilitat enfront la contaminació, i l’escassetat d’aigua potable en moltes regions del planeta, remarca la necessitat d’una bona gestió de l’aigua, aspecte clau pel desenvolupament humà sostenible. La preservació dels recursos hídrics rau principalment en la prevenció de la seva contaminació. Malgrat tot, la creixent generació d’aigües residuals evidencia la necessitat de sistemes de tractament. El tractament de les aigües residuals és una pràctica àmpliament estesa en la majoria dels països desenvolupats. No obstant, és menys comú en comunitats petites i sobretot en països en vies de desenvolupament on, a més, la potabilització de l’aigua és escassa. Aquest fet es deu bàsicament als elevats costos de construcció i explotació de les plantes de tractament convencionals o intensives. Tècniques de tractament alternatives, com ara els sistemes naturals o extensius, es presenten en aquesta ponència com a sistemes pont per a una gestió sostenible en un futur pròxim. Els avantatges principals d’aquest sistemes són: baix o nul consum energètic, baixa generació de residus, baix impacte sonor, bona integració en el medi, simple operació i fàcil manteniment. Per contra, les principals limitacions són el requeriment de grans superfícies i el cost de construcció (lligat als moviments de terra). Degut a què el control operacional és bastant limitat, les fases de disseny i de construcció són crucials per a assegurar un bon funcionament i una bona qualitat de l’aigua depurada.Peer Reviewe

Estrategias de mejora del bienestar psicológico de los estudiantes universitarios

Novenes Jornades de Foment de la Investigació de la FCHS (Any 2003-2004)El objetivo de este trabajo es doble: por una parte analizar los niveles de burnout en estudiantes de Psicología así como sus niveles de satisfacción, y por otra, diseñar un programa de mejora u optimización del bienestar psicológico de los estudiantes que han participado en el estudio. Para conseguir este doble objetivo, el estudio estaba constituido por dos partes claramente diferenciadas. En la primera de ellas, se evaluó la salud psicosocial de los estudiantes participantes del estudio (burnout y satisfacción) a través de cuestionarios estructurados. La muestra final estaba formada por 15 estudiantes de entre 21 y 24 años, que cursaban 4º curso de Psicología en la Universitat Jaume I de Castellón. Los resultados indicaron que los estudiantes de nuestra muestra no tienen burnout, por lo que la intervención la dirigimos hacia el desarrollo de una serie de estrategias de optimización para la mejora del bienestar, la satisfacción y la autoeficacia. Con el fin de diseñar estas estrategias de optimización, se llevó a cabo un estudio de caso (a 14 de los participantes del estudio) mediante la técnica de Entrevistas de Incidentes Críticos para conocer cuales eran los desencadenantes del estrés en estudiantes. Esto permitió averiguar cuáles eran las situaciones en las que se podría intervenir para optimizar las estrategias de afrontamiento de los sujetos. Las estrategias planteadas para los estudiantes se muestran en la última parte del estudio

Image-based multi-agent reinforcement learning for demand–capacity balancing

Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures on both airports and airlines operations. Thus, the central flow management unit continually seeks to improve traffic flow management to reduce delays and congestion. In this work, we investigated the use of reinforcement learning (RL) methods to compute policies to solve demand–capacity imbalances (a.k.a. congestion) during the pre-tactical phase. To address cases where the expected demands exceed the airspace sector capacity, we considered agents representing flights who have to decide on ground delays jointly. To overcome scalability issues, we propose using raw pixel images as input, which can represent an arbitrary number of agents without changing the system’s architecture. This article compares deep Q-learning and deep deterministic policy gradient algorithms with different configurations. Experimental results, using real-world data for training and validation, confirm the effectiveness of our approach to resolving demand–capacity balancing problems, showing the robustness of the RL approach presented in this article.This work was funded by EUROCONTROL under Ph.D. Research contract no. 18-220569- C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under grant number PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Near Remote Sensing for Tactical Earth Protection

In this paper we present how to use an Unmanned Aerial System in remote sensing. The system is specifically designed for forest fire management, as a support tool for the Fire Services to improve their tactical decisions. The system payload includes two cameras: a thermal camera and a visual camera. A simple image processing algorithm is applied to the thermal images in order to detect hot areas. In case of detecting a hot spot, it raises an event and notifies the geographical position of the spot, so that the firemen manager can know the hot spot position as soon as possible. On demand, the system also provides the visual image of the area with the shape of the detected hot spot marked on it. The visual images of the surroundings of the fire can help experts to discard false positives and to make faster and more accurate decisions.Postprint (published version

RNN-CNN hybrid model to predict C-ATC CAPACITY regulations for en-route traffic

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictionsThis work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber PID2020-116377RB-C21. This project has also received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 783287.Peer ReviewedPostprint (published version

General queuing model for optimal seamless delivery of payload processing in multi-core processors

This is a pre-print of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-017-2109-4.Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multi-core boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.Peer ReviewedPostprint (author's final draft

Relación entre la hepatitis crónica vírica B y/o C y el liquen plano bucal

Introducción: La hepatopatía crónica es una patología producida, principalmente, por la ingesta alcohólica y por la infección de los virus de la hepatitis B y/o C. En los últimos años se ha discutido ampliamente la posible asociación entre la hepatopatía crónica y el liquen plano bucal. Recientemente, se ha especulado que la asociación entre liquen plano bucal y la enfermedad hepática tiene una base de origen vírica. Material y método: El objetivo de este estudio transversal emparejado es conocer si existe relación entre las hepatitis víricas B y/o C crónicas y el liquen plano bucal. Para ello, se seleccionaron dos grupos de 100 individuos cada uno: un grupo de casos, formado por pacientes infectados con el virus de la hepatitis B y/o C y un grupo control, equiparado en edad y sexo al grupo de casos, cuyos pacientes no padecían hepatopatía alguna. Se exploró la cavidad bucal, principalmente para detectar lesiones de liquen plano en ambos grupos, aunque se registró cualquier otra alteración de la mucosa bucal. Resultados: No se encontró ningún paciente del grupo de casos con liquen plano bucal, siendo cuatro los individuos que padecían esta patología en el grupo control. Conclusiones: En nuestro estudio no se halló ninguna asociación entre la infección por el virus de la hepatitis B y/o C y el liquen plano bucal.Introduction: The chronic liver disease is a pathology produced, mainly, by the alcohol chronic abuse and by the hepatitis B and/or C virus infection. In the last years, it has been widely discussed the possible association between chronic liver disease and oral lichen planus. Recently, it has been suggested that the association between oral lichen planus and liver disease has a viral origin. Material and method: The objective of this transversal matched study is to know if there is a relationship between B and/or C viral chronic hepatitis and oral lichen planus. Two groups of 100 patients were selected: a case group with patients infected with hepatitis B and/or C virus, and a control group without liver disease matched in age and gender. Oral cavity was explored to detect lichen planus in both groups, but we registered other mucosal alterations. Results: We did not found any patient of the case group with oral lichen planus, but four patients with this disease in the control group. Conclusions: In our study we did not found any association between the infection with the hepatitis B and/or C virus and oral lichen planus

Virtualizing super-computation on-board UAS

Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.Postprint (published version

Understanding the implications of the future unmanned air traffic growth

In the next years, the unmanned air business is expected to have an average annual growth rate of 14.5 per cent. Last-mile delivery, inspection works and security tasks are the most expected missions that those unmanned aircraft (UA) will execute. Most of these missions are well suited for multi-copters: small airframes with vertical take-off and landing capabilities. Large fleets of UA will be managed by new aerial logistic centers where flight plans will be created and monitored, the payload will be prepared, and fast battery replacement will allow continuous flights to obtain maximum benefit. Beyond visual line-of-sight capabilities is a must for those logistic center businesses. Anticipating the scalability of unmanned aircraft growth is the aim of this paper. For this, a simulation tool has been developed which generates unmanned traffic flights from completely parameterized inputs: the geographic area and the type and number of operations, aircraft and operators. For this paper, the tested scenario is a logistics industrial polygon with increasing delivery traffic, the Martorell industrial area (5.8 km2). Flights have a random altitude from 80 m to 120 m. En- route phases have some slight turns to make them more realistic. The time of departure follows a Box-Muller algorithm during the declared business hours, centered in the peak declared hour.This work was funded by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber TRA2016-77012-RPeer ReviewedPostprint (published version

Decision support system for hot spot detection

Postprint (published version