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Publikasjoner (10 av 166) Visa alla publikasjoner
Li, H., Zhang, Y., Yang, C., Gao, R., Ding, F., Olofsson, T., . . . Li, A. (2024). Sleep microenvironment improvement for the acute plateau entry population through a novel nasal oxygen supply system. Building and Environment, 256, Article ID 111467.
Åpne denne publikasjonen i ny fane eller vindu >>Sleep microenvironment improvement for the acute plateau entry population through a novel nasal oxygen supply system
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2024 (engelsk)Inngår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 256, artikkel-id 111467Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Most people who have moved to high-altitude areas temporarily suffer from sleep disorders. Sleep deprivation negatively affects not only people's daytime activities but also their health. However, most of the existing nonpharmaceutical intervention methods have the problems of discomfort, restricted movement, or high cost. This study involved the use of an oxygen-rich flow of air in the breathing area during sleep to fight hypoxia and aid with altitude acclimatization when people first traveled to a highland plateau. The associated nasal breathing targeted oxygen supply system (NBTOSS) was designed and optimized by numerical simulation and full-scale experiments. Blood oxygen saturation (SaO2) and pulse rate (PR) monitoring experiments were conducted on subjects exposed to hypoxia at a high altitude (Lhasa, 3646.31 m) with or without assistance from the novel oxygen system and on a lowland plain (Xi'an, 397.5 m) as a comparison. The size of the affected area, concentration target value, and oxygen consumption were used as evaluation indices. Experiments have demonstrated the feasibility of creating an oxygen-enriched microenvironment in breathing area during sleep. The results of the testing showed that the oxygen supply area was uniformly covered and that the degree of hypoxia in subjects was effectively alleviated, with average SaO2 increasing to 95% ± 1%. Maintaining oxygen levels during sleep for temporary residents of high altitudes with less oxygen consumption and minimal oxygen supply costs is discussed to provide a healthy and comfortable oxygen-enriched environment.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
High-altitude areas, Microenvironment creation, Oxygen enrichment, Personalized air distribution, Sleep environment
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-223231 (URN)10.1016/j.buildenv.2024.111467 (DOI)2-s2.0-85189445111 (Scopus ID)
Tilgjengelig fra: 2024-04-19 Laget: 2024-04-19 Sist oppdatert: 2024-04-19bibliografisk kontrollert
Liu, B., Lu, W., Olofsson, T., Zhuang, X. & Rabczuk, T. (2024). Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites. Composite structures, 327, Article ID 117601.
Åpne denne publikasjonen i ny fane eller vindu >>Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
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2024 (engelsk)Inngår i: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 327, artikkel-id 117601Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Polymeric graphene-enhanced composites (PGECs), Interpretable Integrated Learning, Stochastic multi-scale modeling, Thermal properties, Data-driven technique
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-215912 (URN)10.1016/j.compstruct.2023.117601 (DOI)2-s2.0-85175088621 (Scopus ID)
Forskningsfinansiär
The Kempe Foundations, JCK-2136EU, Horizon 2020, 101016854J. Gust. Richert stiftelse, 2023-00884Swedish Research Council, 2018-05973Swedish Research Council, 2022-06725
Tilgjengelig fra: 2023-10-29 Laget: 2023-10-29 Sist oppdatert: 2023-11-10bibliografisk kontrollert
Man, Q., Yu, H., Feng, K., Olofsson, T. & Lu, W. (2024). Transfer of building retrofitting evaluations for data-scarce conditions: an empirical study for Sweden to China. Energy and Buildings, 310, Article ID 114041.
Åpne denne publikasjonen i ny fane eller vindu >>Transfer of building retrofitting evaluations for data-scarce conditions: an empirical study for Sweden to China
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2024 (engelsk)Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 310, artikkel-id 114041Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Building retrofitting, Data-driven model, Energy-efficient, Transfer learning
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-223261 (URN)10.1016/j.enbuild.2024.114041 (DOI)2-s2.0-85187962486 (Scopus ID)
Forskningsfinansiär
Swedish Research Council Formas, 2020-02085Swedish Research Council Formas, 2022-01475Swedish Energy Agency, P2022-00141
Tilgjengelig fra: 2024-04-18 Laget: 2024-04-18 Sist oppdatert: 2024-04-18bibliografisk kontrollert
Hu, S., Qiu, S., Feng, K., Man, Q., Olofsson, T. & Lu, W. (2023). A data-driven exploration of the relations between occupant behaviors and comfort performances of energy-efficient measures. In: Yaowu Wang; Feng Lan; Geoffrey Q. P. Shen (Ed.), ICCREM 2023: the human-centered construction transformation - proceedings of the international conference on construction and real estate management 2023. Paper presented at 2023 International Conference on Construction and Real Estate Management: The Human-Centered Construction Transformation, ICCREM 2023, Xi'an, China, 23-24 September, 2023. (pp. 592-604). American Society of Civil Engineers (ASCE)
Åpne denne publikasjonen i ny fane eller vindu >>A data-driven exploration of the relations between occupant behaviors and comfort performances of energy-efficient measures
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2023 (engelsk)Inngår i: ICCREM 2023: the human-centered construction transformation - proceedings of the international conference on construction and real estate management 2023 / [ed] Yaowu Wang; Feng Lan; Geoffrey Q. P. Shen, American Society of Civil Engineers (ASCE), 2023, s. 592-604Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Energy-efficient building retrofitting plays a crucial role in reducing energy consumption and carbon emissions within the building sector. Energy-efficient retrofitting brings about changes in the built environment and it could influence the occupant behaviors. Additionally, occupant behaviors, in turn, alter the indoor environment, thereby affecting the comfort performance of the building after retrofitting. To explore this intricate relation between occupant behaviors and comfort performances of energy-efficient measures, this paper employs a data-driven approach to compile a comprehensive dataset encompassing occupant behaviors, energy-efficient measures, and associated indoor comfort of an office building in Umeå University, Sweden. Multiple binary logistic regression is applied to quantify the relationship between occupant behaviors and comfort performances of energy-efficient measures. The findings of this study hold significant value, providing guidance for occupants in adapting to energy-efficient measures while also informing future retrofitting implementation.

sted, utgiver, år, opplag, sider
American Society of Civil Engineers (ASCE), 2023
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-219498 (URN)10.1061/9780784485217.058 (DOI)2-s2.0-85181534282 (Scopus ID)9780784485217 (ISBN)
Konferanse
2023 International Conference on Construction and Real Estate Management: The Human-Centered Construction Transformation, ICCREM 2023, Xi'an, China, 23-24 September, 2023.
Tilgjengelig fra: 2024-01-25 Laget: 2024-01-25 Sist oppdatert: 2024-01-25bibliografisk kontrollert
Penaka, S. R., Feng, K., Rebbling, A., Azizi, S., Lu, W. & Olofsson, T. (2023). A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden. In: SBE23-Thessaloniki: Sustainable built environments: Paving the way for achieving the targets of 2030 and beyond. Paper presented at 2023 Sustainable Built Environments: Paving the Way for Achieving the Targets of 2030 and Beyond, SBE23-Thessaloniki, Online, March 22-24, 2023. Institute of Physics (IOP), Article ID 012005.
Åpne denne publikasjonen i ny fane eller vindu >>A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden
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2023 (engelsk)Inngår i: SBE23-Thessaloniki: Sustainable built environments: Paving the way for achieving the targets of 2030 and beyond, Institute of Physics (IOP), 2023, artikkel-id 012005Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Vasterbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user's building performance using two benchmark references i.e., 1) Sweden's target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Vasterbotten.

sted, utgiver, år, opplag, sider
Institute of Physics (IOP), 2023
Serie
IOP Conference Series: Earth and Environmental Science, ISSN 1755-1307, E-ISSN 1755-1315 ; 1196
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-212813 (URN)10.1088/1755-1315/1196/1/012005 (DOI)2-s2.0-85166560520 (Scopus ID)
Konferanse
2023 Sustainable Built Environments: Paving the Way for Achieving the Targets of 2030 and Beyond, SBE23-Thessaloniki, Online, March 22-24, 2023
Forskningsfinansiär
EU, Horizon 2020, 101016854Swedish Research Council Formas, 2020-02085
Tilgjengelig fra: 2023-08-16 Laget: 2023-08-16 Sist oppdatert: 2023-08-16bibliografisk kontrollert
Lu, C., Li, S., Gu, J., Lu, W., Olofsson, T. & Ma, J. (2023). A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners. Journal of Building Engineering, 64, Article ID 105602.
Åpne denne publikasjonen i ny fane eller vindu >>A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners
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2023 (engelsk)Inngår i: Journal of Building Engineering, E-ISSN 2352-7102, Vol. 64, artikkel-id 105602Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Integrating renewable energy is a promising solution for buildings to achieve the net-zero-energy goal. Expanding real-time matching between renewable energy generation and building energy demand can help realize more enormous zero-energy potential in practice. However, there are few studies to investigate the real-time energy matching in renewable energy building design. Therefore, in this study, a hybrid ensemble learning framework is proposed for analyzing and predicting zero-energy potential in the real-time matching of photovoltaic direct-driven air conditioner (PVAC) systems. First, the datasets of zero-energy probability (ZEP) are generated under the three main climate regions in China, which are with consideration of the load flexibility of air conditioners and based on six important design variables. Second, a novel ensemble learning method named Extreme Gradient Boosting (XGBoost) is selected to predict ZEP and the Bayesian Optimization (BO) is adopted to identify the optimal hyperparameters and further improve the prediction performance. The statistical analysis shows that ZEP distributions are very different from one region to another one and the PVAC systems in Beijing are the easiest to achieve the zero-energy goal. Among all the variables, PV capacity is the most significant and positively related to ZEP. The prediction results show BO-XGBoost achieves more than 99% accuracy and outperforms other benchmark models in the ZEP prediction of three cities. In a word, this paper reveals BO-XGBoost is the most effective model for ZEP prediction and provides the framework for designers to utilize zero-energy potential analysis and prediction for the first time.

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
Bayesian optimization, Machine learning, Photovoltaic direct-driven air conditioners, Thermal comfort, Zero energy potential
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-201454 (URN)10.1016/j.jobe.2022.105602 (DOI)000997281000001 ()2-s2.0-85142748107 (Scopus ID)
Tilgjengelig fra: 2022-12-06 Laget: 2022-12-06 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Wikman, T., Olofsson, T. & Nair, G. (2023). A literature review on life cycle analysis of buildings.
Åpne denne publikasjonen i ny fane eller vindu >>A literature review on life cycle analysis of buildings
2023 (engelsk)Rapport (Annet vitenskapelig)
Abstract [en]

Life cycle analysis (LCA) can be utilized to evaluate environmental impacts from the construction sector. In Sweden, from January 2022, climate declarations are mandatory when constructing new buildings. This report provides a literature review on various aspects related to LCA inbuilding, with focus on challenges and possibilities. Major challenges with LCA conductance are that buildings have long life-spans which introduce uncertainties in the LCA calculations since parameters may change over time. Choice of calculation tool, system boundaries for the LCA analysis and deviations between databases are further challenges that affect LCA results. Problems with data quality are another issue since usage of generic data may lower the accuracy of LCA studies on local level. Transparency of calculation tools, LCA methods, approximations and complexity of analysis are further challenges. Furthermore, when different LCA methods and calculation tools have been used, comparability between LCA studies can be compromised. To counteract the challenges voices have been raised to create national and even global databases to homogenize the data. Thorough and transparent communication of scope, method and system boundaries in LCA studies can counteract the problem with low transparency, deviating results and comparability issues. Using local data instead of generic data can increase data quality and therefore the quality and accuracy of the results.  

Publisher
s. 27
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-205898 (URN)
Merknad

This report is prepared as part of the Interreg Nord project "Enhanced Sustainability of Built Environment by Collaboration and Digitalization" (ESBE).

Tilgjengelig fra: 2023-03-22 Laget: 2023-03-22 Sist oppdatert: 2023-03-23bibliografisk kontrollert
Mattsson, M., Olofsson, T., Lundberg, L., Korda, O. & Nair, G. (2023). An exploratory study on swedish stakeholders’ experiences with positive energy districts. Energies, 16(12), Article ID 4790.
Åpne denne publikasjonen i ny fane eller vindu >>An exploratory study on swedish stakeholders’ experiences with positive energy districts
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2023 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 16, nr 12, artikkel-id 4790Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Positive energy district (PED) is a novel idea aimed to have an annual surplus of renewable energy and net zero greenhouse gas emissions within an area. However, it is still an ambiguous concept, which might be due to the complexity of city district projects with interconnected infrastructures and numerous stakeholders involved. This study discusses various aspects of PED implementation and presents practitioners’ experiences with the PED concept, challenges, and facilitators they have faced with real projects. The study is based on interviews with ten Swedish professionals. The major challenges reported for PED implementation were local energy production and energy flexibility, sub-optimization, legislation, suitable system boundaries, and involvement of stakeholders. Most of the interviewees mentioned improved collaboration, integrated innovative technology, political support, and climate change mitigation goals as important facilitators. The interviewees highlighted the importance of a local perspective and considered each city’s preconditions when developing a PED project. The study emphasizes that to facilitate PED implementation and replication in cities, more knowledge and clarity is required about PED such as on the definition and system boundaries.

sted, utgiver, år, opplag, sider
MDPI, 2023
Emneord
positive energy district, energy transition, sustainable urban development, stakeholder perspective, replication
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-210541 (URN)10.3390/en16124790 (DOI)001014316100001 ()2-s2.0-85163812620 (Scopus ID)
Prosjekter
RESILIENTa Energisystem Kompetenscentrum
Forskningsfinansiär
Swedish Energy Agency, 52686-1
Tilgjengelig fra: 2023-06-22 Laget: 2023-06-22 Sist oppdatert: 2023-08-28bibliografisk kontrollert
Lu, C., Li, S., Penaka, S. R. & Olofsson, T. (2023). Automated machine learning-based framework of heating and cooling load prediction for quick residential building design. Energy, 274, Article ID 127334.
Åpne denne publikasjonen i ny fane eller vindu >>Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
2023 (engelsk)Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 274, artikkel-id 127334Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Reducing the heating and cooling load through energy-efficient building design can help decarbonize the building sector. Heating and cooling load prediction using machine learning (ML) techniques become increasingly important in the rapid assessment of building design variables at the early design stage. However, when applying the ML techniques, it still requires expert knowledge and manually frequent intervention to improve the prediction performance. Hence, this study proposed an automated machine learning (AutoML)-based framework to automatically generate the optimal ML pipelines for heating and cooling load prediction. An experimental dataset of residential buildings was used to evaluate the proposed framework. The proposed framework achieved the best performance with R2 of 0.9965 and RMSE of 0.602 kWh/m2 for heating load prediction, and R2 of 0.9899 and RMSE of 0.973 kWh/m2 for cooling load prediction. The prediction results showed that the proposed framework outperformed the other improved ML models from the representative studies in the last five years. Further, an explainable analysis of the ML models was explored to reveal the relationships between design variables and heating and cooling load. The proposed framework aims at promoting the AutoML-based framework to designers for building energy performance prediction without excessive ML knowledge and manually frequent intervention.

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
Automated machine learning, Energy-efficient building, Heating and cooling load, Residential building design
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-206455 (URN)10.1016/j.energy.2023.127334 (DOI)000966965100001 ()2-s2.0-85151011404 (Scopus ID)
Tilgjengelig fra: 2023-04-06 Laget: 2023-04-06 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Yu, H., Feng, K., Penaka, S. R., Man, Q., Lu, W. & Olofsson, T. (2023). Data-driven modelling of building retrofitting with incomplete physics: a generative design and machine learning approach. Paper presented at NSB 2023, 13th Nordic Symposium on building physics, Aalborg, Denmark, June 12-14, 2023. Journal of Physics, Conference Series, 2654, Article ID 012053.
Åpne denne publikasjonen i ny fane eller vindu >>Data-driven modelling of building retrofitting with incomplete physics: a generative design and machine learning approach
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2023 (engelsk)Inngår i: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 2654, artikkel-id 012053Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Building performance simulation (BPS) based on physical models is a popular method for estimating the expected energy savings from energy-efficient building retrofitting. However, for many buildings, especially older buildings, built several decades ago, an operator do not have full access to the complete information for the BPS method. Incomplete information comes from the lack of detailed building physics, such as the thermal transmittance of some building components due to the deterioration of components over time. To address this challenge, this paper proposed a data-driven approach to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach integrates the backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), and generative design (GD). It generates the required big database of building performance through generative design, which can overcome the problem of incomplete information during building performance simulation and energy-efficient retrofitting. The case study is based on old residential buildings in severe cold regions of China, using the proposed approach to predict energy-efficient retrofitting performance. The results indicated that the proposed approach can model the performance of residential buildings with more than 90% confidence, and show the variation of results. The core contribution of the proposed approach is to provide a way of performance prediction of individual buildings resulting from different retrofitting measures under the incomplete physics condition.

sted, utgiver, år, opplag, sider
Institute of Physics (IOP), 2023
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-220010 (URN)10.1088/1742-6596/2654/1/012053 (DOI)2-s2.0-85181172898 (Scopus ID)
Konferanse
NSB 2023, 13th Nordic Symposium on building physics, Aalborg, Denmark, June 12-14, 2023
Forskningsfinansiär
Swedish Research Council Formas, 2020-02085EU, Horizon 2020, AURORAL
Tilgjengelig fra: 2024-02-02 Laget: 2024-02-02 Sist oppdatert: 2024-02-02bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8704-8538