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Hernandez Bennetts, VictorORCID iD iconorcid.org/0000-0001-5061-5474
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Publications (10 of 43) Show all publications
Hernandez Bennetts, V., Kamarudin, K., Wiedemann, T., Kucner, T. P., Somisetty, S. L. & Lilienthal, A. (2019). Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning. Sensors, 19(5), Article ID E1119.
Open this publication in new window or tab >>Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 5, article id E1119Article in journal (Refereed) Published
Abstract [en]

Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Airflow modeling, environmental monitoring, machine learning, mobile robotics, static sensor networks, ventilation
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-73199 (URN)10.3390/s19051119 (DOI)000462540400138 ()30841615 (PubMedID)2-s2.0-85062613532 (Scopus ID)
Funder
Vinnova, 2017-05468
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-04-10Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2019). Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping. Sensors, 19(3), Article ID 478.
Open this publication in new window or tab >>Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 478Article in journal (Refereed) Published
Abstract [en]

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
Robotics, signal processing, electronics, gas source localization, gas distribution mapping; gas sensors, drone, UAV, MOX sensor, quadcopter
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71963 (URN)10.3390/s19030478 (DOI)000459941200041 ()30682827 (PubMedID)2-s2.0-85060510907 (Scopus ID)
Note

Funding Agency:

Spanish MINECO  BES-2015-071698  TEC2014-59229-R

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-03-19Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2019). Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose. Sensors, 19(3), Article ID E685.
Open this publication in new window or tab >>Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed) Published
Abstract [en]

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-72366 (URN)10.3390/s19030685 (DOI)000459941200248 ()30736489 (PubMedID)2-s2.0-85061226919 (Scopus ID)
Note

Funding Agency:

European Commission  645101

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-03-19Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2018). 3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors. Proceedings, 2(13), Article ID 911.
Open this publication in new window or tab >>3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors
2018 (English)In: Proceedings, E-ISSN 2504-3900, Vol. 2, no 13, article id 911Article in journal (Refereed) Published
Abstract [en]

Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2018
Keywords
MOX, metal oxide, flow visualization, gas sensors, gas distribution mapping, sensor grid, 3D, gas source localization, indoor
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71962 (URN)10.3390/proceedings2130911 (DOI)
Projects
SmokeBot (EC H2020, 645101)
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-02-01Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2018). A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sensors and actuators. B, Chemical, 259, 183-203
Open this publication in new window or tab >>A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
2018 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed) Published
Abstract [en]

Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

Place, publisher, year, edition, pages
Amsterda, Netherlands: Elsevier, 2018
Keywords
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63468 (URN)10.1016/j.snb.2017.10.063 (DOI)000424877600023 ()2-s2.0-85038032167 (Scopus ID)
Projects
SmokBot
Funder
EU, Horizon 2020, 645101
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2019-02-12Bibliographically approved
Kamarudin, K., Shakaff, A. Y., Hernandez Bennetts, V., Mamduh, S. M., Zakaria, A., Visvanathan, R., . . . Kamarudin, L. M. (2018). Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization. Advanced Robotics, 32(17), 903-917
Open this publication in new window or tab >>Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization
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2018 (English)In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 32, no 17, p. 903-917Article in journal (Refereed) Published
Abstract [en]

Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM+V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5-1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2018
Keywords
Gas source localization, gas distribution mapping, SLAM, mobile robot, gas sensing, metal oxide gas sensor
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-69553 (URN)10.1080/01691864.2018.1516568 (DOI)000445798600001 ()2-s2.0-85053600678 (Scopus ID)
Note

Funding Agency:

Universiti Malaysia Perlis  9001-00561

Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2018-10-16Bibliographically approved
Wiedemann, T., Shutin, D., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2017). Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling. In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings. Paper presented at International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, Canada, May 28-31, 2017 (pp. 122-124). IEEE
Open this publication in new window or tab >>Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling
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2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 122-124Conference paper, Published paper (Refereed)
Abstract [en]

Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-60688 (URN)10.1109/ISOEN.2017.7968884 (DOI)978-1-5090-2393-6 (ISBN)978-1-5090-2392-9 (ISBN)
Conference
International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, Canada, May 28-31, 2017
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2019-03-29Bibliographically approved
Kamarudin, K., Hernandez Bennetts, V., Mamduh, S. H., Visvanathan, R., Yeon, A. S., Shakaff, A. Y., . . . Kamarudin, L. M. (2017). Cross-sensitivity of Metal Oxide Gas Sensor to Ambient Temperature and Humidity: Effects on Gas Distribution Mapping. In: Proceedings of the 11th Asian Conference on Chemical Sensors: . Paper presented at 11th Asian Conference on Chemical Sensors (ACCS 2015), Penang, Malaysia, November 16-18, 2015. American Institute of Physics (AIP), 1808, Article ID UNSP 020025-1.
Open this publication in new window or tab >>Cross-sensitivity of Metal Oxide Gas Sensor to Ambient Temperature and Humidity: Effects on Gas Distribution Mapping
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2017 (English)In: Proceedings of the 11th Asian Conference on Chemical Sensors, American Institute of Physics (AIP), 2017, Vol. 1808, article id UNSP 020025-1Conference paper, Published paper (Refereed)
Abstract [en]

Metal oxide gas sensors have been widely used in robotics application to perform remote and mobile gas sensing. However, previous researches have indicated that this type of sensor technology is cross-sensitive to environmental temperature and humidity. This paper therefore investigates the effects of these two factors towards gas distribution mapping and gas source localization domains. A mobile robot equipped with TGS2600 gas sensor was deployed to build gas distribution maps of indoor environment, where the temperature and humidity varies. The results from the trials in environment with and without gas source indicated that there is a strong relation between the fluctuation of the mean and variance map with respect to the variations in the temperature and humidity maps.

Place, publisher, year, edition, pages
American Institute of Physics (AIP), 2017
Series
AIP Conference Proceedings, ISSN 0094-243X
Keywords
Chemical sensors, Metal oxide sensors, sensor calibration, mobile robots, environmental monitoring
National Category
Computer Sciences Chemical Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-47931 (URN)10.1063/1.4975258 (DOI)000409359600025 ()2-s2.0-85016952340 (Scopus ID)9780735414761 (ISBN)
Conference
11th Asian Conference on Chemical Sensors (ACCS 2015), Penang, Malaysia, November 16-18, 2015
Note

Funding Agencies:

UniMAP  

Ministry of Higher Education, Malaysia (MOHE) 

Available from: 2016-02-04 Created: 2016-02-04 Last updated: 2019-04-10Bibliographically approved
Kucner, T. P., Magnusson, M., Schaffernicht, E., Hernandez Bennetts, V. M. & Lilienthal, A. (2017). Enabling Flow Awareness for Mobile Robots in Partially Observable Environments. IEEE Robotics and Automation Letters, 2(2), 1093-1100
Open this publication in new window or tab >>Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed) Published
Abstract [en]

Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Field robots; mapping; social human-robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-55174 (URN)10.1109/LRA.2017.2660060 (DOI)000413736600094 ()
Projects
ILIAD
Funder
Knowledge Foundation, 20140220 20130196
Note

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-11-23Bibliographically approved
Vuka, M., Schaffernicht, E., Schmuker, M., Hernandez Bennetts, V., Amigoni, F. & Lilienthal, A. J. (2017). Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot: A Gaussian Regression Bout Amplitude Approach. In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings. Paper presented at IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, QC, Canada, May 28-31, 2017 (pp. 164-166). IEEE
Open this publication in new window or tab >>Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot: A Gaussian Regression Bout Amplitude Approach
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2017 (English)In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 164-166Conference paper, Published paper (Refereed)
Abstract [en]

Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-60672 (URN)10.1109/ISOEN.2017.7968898 (DOI)2-s2.0-85027226540 (Scopus ID)
Conference
IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017), Montreal, QC, Canada, May 28-31, 2017
Available from: 2017-09-08 Created: 2017-09-08 Last updated: 2018-08-06Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-5061-5474

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