Publications

Strategies and their implementation. NILU F

Tørseth, K.

Strategies for grouping per-and polyfluoroalkyl substances

Cousins, Ian T.; Glüge, Juliane; Goldenman, G.; Herzke, Dorte; Lohmann, R.; Miller, M.; Ng, C. A.; Scheringer, M.; Trier, X.; Wang, Z.; DeWitt, J. C.

Strategy for national satellite related operational support to aviation for volcanic ash avoidance NILU OR

Tørseth, K.; Hov, Ø.

Stratosphere-Mesosphere coupling during major stratospheric sudden warming. NILU F

Orsolini, Y.; Tweedy, O.; Limpasuvan, V.; Smith, A.; Kvissel, O.-K.

Stratospheric aerosol data records for the climate change initiative: Development, validation and application to chemistry-climate modelling.

Bingen, C.; Robert, C. E.; Stebel, K.; Brühl, C.; Schallock, J.; Vanhellemont, F.; Mateshvili, N.; Höpfner, M.; Trickl, T.; Barnes, J. E.; Jumelet, J.; Vernier, J.-P.; Popp, T.; de Leeuw, G.; Pinnock, S.

Stratospheric effects of energetic particle precipitation in 2003-2004.

Randall, C.E.; Harvey, V.L.; Manney, G.L.; Orsolini, Y.; Codrescu, M.; Sioris, C.; Brohede, S.; Haley, C.S.; Gordley, L.L.; Zawodny, J.M.; Russell, J.M.

Stratospheric ozone and the link to climate change. EUR 19867

Austin, J.; Langematz, U. Contributing authors: Dameris, M.; Pawson, S.; Pitari, G.; Shine, K.P.; Stordal, F.

Stratospheric ozone during the arctic winter: Brewer measurements in Ny-Ålesund.

Rafanelli, C.; De Simone, S.; Damiani, A.; Myhre, C.L.; Edvardsen, K.; Svenoe, T.; Benedetti, E.

Street Emission Ceiling (SEC) exercise. Phase 3 report on station pair data analysis, comparison with emissions estimates, street typology and guidance on how to use it. ETC/ACC Technical paper, 2006/7

Larssen, S.; Mellios, G.; van den Hout, D.; Kalognomou, E.A.; Moussiopoulos, N.

Street Emission Ceiling exercise - Phase 2 report. ETC/ACC Technical paper, 2004/5

Moussiopoulos, N.; Kalognomou, E.-A.; Papathanasiou, A.; Eleftheriadou, S.; Barmpas, P.; Vlachokostas, C.; Samaras, Z.; Mellios, G.; Vouitsis, I.; Larssen, S.E.; Gjerstad, K.I.; de Leeuw, F.A.A.M.; van den Hout, K.D.; Teeuwisse, S.; van Aalst, R.M.

Strengthened linkage between midlatitudes and Arctic in boreal winter

Xu, Xinping; He, Shengping; Gao, Yongqi; Furevik, Tore; Wang, Huijun; Li, Fei; Ogawa, Fumiaki

Springer

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer

Chatterjee, Ayan; Riegler, Michael Alexander; Ganesh, K.; Halvorsen, Pål

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.

Nature Portfolio

Strongly increasing blood concentrations of lipid-soluble organochlorines in high Arctic common eiders during incubation fast. NILU F

Moe, B.; Bustnes, J.O.; Herzke, D.; Hanssen, S.A.; Nordstad, T.; Sagerup, K.