Radon Mitigation Approach in a Laboratory Measurement Room

Blanco-Rodríguez, Patricia; Fernández-Serantes, Luis Alfonso; Otero-Pazos, A.; Calvo-Rolle, José Luís; Juez, Francisco Javier de Cos

doi:10.3390/s17051090

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…It derives from the decay of radium and uranium and it is naturally emitted from soil and rocks and transported through water, or environmental carrier gases. It represents half of the radiation exposure to human being and a long-term contact could induce lung cancer [129][130][131].…”

Section: Target Gasesmentioning

confidence: 99%

Year 2020: A Snapshot of the Last Progress in Flexible Printed Gas Sensors

Fioravanti

Carotta

2020

Applied Sciences

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A review of recent advances in flexible printed gas sensors is presented. During the last years, flexible electronics has started to offer new opportunities in terms of sensors features and their possible application fields. The advent of this technology has made sensors low-cost, thin, with a large sensing area, lightweight, wearable, flexible, and transparent. Such new characteristics have led to the development of new gas sensor devices. The paper makes some statistical remarks about the research and market of the sensors and makes a shot of the printing technologies, the flexible organic substrates, the functional materials, and the target gases related to the specific application areas. The conclusion is a short notice on perspectives in the field.

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Section: Target Gasesmentioning

confidence: 99%

Year 2020: A Snapshot of the Last Progress in Flexible Printed Gas Sensors

Fioravanti

Carotta

2020

Applied Sciences

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“…A radioactive laboratory was the place chosen by Blanco et al [ 50 ] to measure radon concentration levels. Specifically, the work described in the paper was aimed at reducing the interference of radon with different tasks performed in a measurement room of the lab.…”

Section: Related Workmentioning

confidence: 99%

A Cost-Effective IoT System for Monitoring Indoor Radon Gas Concentration

Blanco-Novoa

Fernández‐Caramés

Fraga‐Lamas

et al. 2018

Sensors

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Radon is a noble gas originating from the radioactive decay chain of uranium or thorium. Most radon emanates naturally from the soil and from some building materials, so it can be found in many places around the world, in particular in regions with soils containing granite or slate. It is almost impossible for a person to detect radon gas without proper tools, since it is invisible, odorless, tasteless and colorless. The problem is that a correlation has been established between the presence of high radon gas concentrations and the incidence of lung cancer. In fact, the World Health Organization (WHO) has stated that the exposure to radon is the second most common cause of lung cancer after smoking, and it is the primary cause of lung cancer among people who have never smoked. Although there are commercial radon detectors, most of them are either expensive or provide very limited monitoring capabilities. To tackle such an issue, this article presents a cost-effective IoT radon gas remote monitoring system able to obtain accurate concentration measurements. It can also trigger events to prevent dangerous situations and to warn users about them. Moreover, the proposed solution can activate mitigation devices (e.g., forced ventilation) to decrease radon gas concentration. In order to show its performance, the system was evaluated in three different scenarios corresponding to representative buildings in Galicia (Spain), a region where high radon gas concentrations are common due to the composition of the soil. In addition, the influence of using external hardware (i.e., WiFi transceivers and an embedded System-on-Chip (SoC)) next to the radon gas sensor is studied, concluding that, in the tested scenarios, they do not interfere with the measurements.

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“…But there are lots of different ideas to improve the performance of CNNs that could be applied to solar AO, like the use of recurrent neural networks [35], on-line training [36], [37] or even apply the notion of classification when computing the outputs. For last, it is interesting to keep in mind that in astronomy, not only AO could be benefited for the usage of neural networks since, for instance, the detection of exoplanets [38], [39] has already done.…”

Section: Conclusion and Future Linesmentioning

confidence: 99%

An approach using deep learning for tomographic reconstruction in solar observation

Gómez¹,

Gutiérrez²,

Lasheras³

et al. 2017

Proceedings of the Adaptive Optics for Extremely Large Telescopes 5

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Solar observations with large telescopes have to face numerous challenges for its implementation. Adapting the current reconstructor systems of adaptive optics, which were developed for night observations to remove the atmospheric turbulence, is one of them. Neural networks were proved as a great solution in different fields, as image processing or in tomographic reconstruction. In this paper, possible uses of neural networks in solar adaptive optics will be explored, providing some early results of their application to different fields of adaptive optics.

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Radon Mitigation Approach in a Laboratory Measurement Room

Cited by 9 publications

References 12 publications

Year 2020: A Snapshot of the Last Progress in Flexible Printed Gas Sensors

Year 2020: A Snapshot of the Last Progress in Flexible Printed Gas Sensors

A Cost-Effective IoT System for Monitoring Indoor Radon Gas Concentration

An approach using deep learning for tomographic reconstruction in solar observation

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