Use of metal oxide semiconductor sensors to measure methane in aquatic ecosystems in the presence of cross-interfering compounds

Monitoring dissolved methane in aquatic ecosystems contributes significantly to advancing our understanding of the carbon cycle in these habitats and capturing their impact on methane emissions. Low-cost metal oxide semiconductors (MOS) gas sensors are becoming an increasingly attractive tool to perform such measurements, especially at the air–water interface. However, the performance of MOS sensors in aquatic environmental sciences has come under scrutiny because of their cross-sensitivity to temperature, moisture, and sulfide interference. In this study, we evaluated the performance and limitations of a MOS methane sensor when measuring dissolved methane in waters. A MOS sensor was encapsulated in a hydrophobic extended polytetrafluoroethylene membrane to impede contact with water but allow gas perfusion. Therefore, the membrane enabled us to submerge the sensor in water and overcome cross-sensitivity to humidity. A simple portable, low-energy, flow-through cell system was assembled that included an encapsulated MOS sensor and a temperature sensor. Waters (with or without methane) were injected into the flow cell at a constant rate by a peristaltic pump. The signals from the two sensors were recorded continuously with a cost-efficient microcontroller. Tests specifically focused on the effect of water temperature and sulfide interference on sensor performance. Our experiments revealed that the lower limit of the sensor was in the range of 0.1–0.2 µmol¿L-1 and that it provided a stable response at water temperatures in the range of 18.5–28°C. Dissolved sulfide at a concentration of 0.4¿mmol¿L-1 or higher interfered with the sensor response, especially at low methane concentrations (0.5 µmol¿L-1 or lower). However, we show that if dissolved sulfide is monitored, its interference can be alleviated.Postprint (published version

Learning of chunking sequences in cognition and behavior

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson's disease and Schizophrenia

Improving calibration of chemical gas sensors for fire detection using small scale setups

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system.Postprint (author's final draft

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system

Discontinuously operated MOX sensors for low power applications

Metal oxide semiconductor sensors are limited by their low selectivity, high power consumption and temporal drift. This paper proposes a novel discontinuous temperature modulation operation mode characterized by ondemand measurements and periodic warmup cycles.The performance of two sets of FIS SB-500-12 sensors, one group continuously operated and the other group discontinuously operated, was compared in a scenario of carbon monoxide detection at low concentrations for five consecutive days. Results showed that the discontinuous operating mode moderately increased the prediction error and the limit of detection but was advantageous in terms of energy savings (up to 60% with respect to the continuous temperature modulation mode) .Postprint (author's final draft

Machine learning methods in electronic nose analysis

The main existent tool to monitor chemical environ- ments in a continuous mode is gas sensor arrays, which have been popularized as electronic noses (enoses). To design and validate these monitoring systems, it is necessary to make use of machine learning techniques to deal with large amounts of heterogeneous data and extract useful information from them. Therefore, enose data present several challenges for each of the steps involved in the design of a machine learning system. Some of the machine learning tasks involved in this area of research include generation of operational patterns, detection anomalies, or classification and discrimination of events. In this work, we will review some of the machine learning approaches adopted in the literature for enose data analysis, and their application to three different tasks: single gas classification under tightly-controlled operating conditions, gas binary mixtures classification in a wind tunnel with two independent gas sources, and human activity monitoring in a NASA spacecraft cabin simulator.Postprint (author's final draft

Mapping layperson medical terminology into the Human Phenotype Ontology using neural machine translation models

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.eswa.2022.117446.In the medical domain there exists a terminological gap between patients and caregivers and the healthcare professionals. This gap may hinder the success of the communication between healthcare consumers and professionals in the field, with negative emotional and clinical consequences. In this work, we build a machine learning-based tool for the automatic translation between the terminology used by laypeople and that of the Human Phenotype Ontology (HPO). HPO is a structured vocabulary of phenotypic abnormalities found in human disease. Our method uses a vector space to represent an HPO-specific embedding as the output space for a neural network model trained on vector representations of layperson versions and other textual descriptors of medical terms. We explored different output embeddings coupled to different neural network architectures for the machine translation stage. We compute a similarity measure to evaluate the ability of the model to assign an HPO term to a layperson input. The best-performing models resulted with a similarity higher than 0.7 for more than 80% of the terms, with a median between 0.98 and 1. The translator model is made available in a web application at this link: https://hpotranslator.b2slab.upc.edu.This work was supported by the Spanish Ministry of Economy and Competitiveness (www.mineco.gob.es) TEC2014-60337-R, DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), and Share4Rare project (Grant Agreement 780262). This work was partially funded by ACCIÓ (Innotec ACE014/20/000018). B2SLab is certified as 2017 SGR 952. The authors thank the NVIDIA Corporation for the donation of a Titan Xp GPU used to run the models presented in this article. J. Fonollosa acknowledges the support from the Serra Húnter program.Peer ReviewedPostprint (published version

Home monitoring for older singles: A gas sensor array system

Many residential environments have been equipped with sensing technologies both to provide assistance to older people who have opted for aging-in-place and to provide information to caregivers and family. However, such technologies are often accompanied by physical discomfort, privacy concerns, and complexity of use. We explored the feasibility of monitoring home activity using chemical sensors that pose fewer privacy concerns than, for example, video-cameras and which do not suffer from blind spots. We built a monitoring device that integrates a sensor array and IoT capabilities to gather the necessary data about a resident in his/her living space. Over a period of 3 months, we uninterruptedly measured the living space of a typical elder person living on his/her own. To record the level of activity during the same period and obtain a ground truth for the activity, a set of motion sensors were also deployed in the house. Home activity was extracted from a PCA space moving-window which translated sensor data into the event space; this also compensated for environmental and sensor drift. Our results show that it is possible to monitor the person’s home activity and detect sudden deviations from it using a low-cost, non-invasive, system based on gas sensors that gather data on the air composition in the living space. We made the dataset publicly available at https://archive.ics.uci.edu/ml/index.php2.This work was supported by the Spanish Ministry of Economy and Competitiveness (www.mineco.gob.es) PID2021-122952OB-I00, DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), Share4Rare project (Grant Agreement 780262), ISCIII (grant AC22/00035), ACCIÓ (grant Innotec ACE014/20/000018) and Pla de Doctorats Industrials de la Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (2022 DI 014), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie (grant No. 101029808). JF also acknowledges the CERCA Program/Generalitat de Catalunya and the Serra Húnter Program. B2SLab is certified as 2017 SGR 952.Peer ReviewedPostprint (author's final draft

Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization

Inherent variability of chemical sensors makes it necessary to calibrate chemical detection systems individually. This shortcoming has traditionally limited usability of systems based on metal oxide gas sensor arrays and prevented mass-production for some applications. Here, aiming at exploring calibration transfer between chemical sensor arrays, we exposed five twin 8-sensor detection units to different concentration levels of ethanol, ethylene, carbon monoxide, or methane. First, we built calibration models using data acquired with a master unit. Second, to explore the transferability of the calibration models, we used Direct Standardization to map the signals of a slave unit to the space of the master unit in calibration. In particular, we evaluated the transferability of the calibration models to other detection units, and within the same unit measuring days apart. Our results show that signals acquired with one unit can be successfully mapped to the space of a reference unit. Hence, calibration models trained with a master unit can be extended to slave units using a reduced number of transfer samples, diminishing thereby calibration costs. Similarly, signals of a sensing unit can be transformed to match sensor behavior in the past to mitigate drift effects. Therefore, the proposed methodology can reduce calibration costs in mass-production and delay recalibrations due to sensor aging. Acquired dataset is made publicly available

A practical method to estimate the resolving power of a chemical sensor array: application to feature selection

A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.Peer ReviewedPostprint (published version