K. Dickstein, A. Cohen‐Solal, G. Filippatos
et al.
Authors/Task Force Members: Kenneth Dickstein (Chairperson) (Norway)*, Alain Cohen-Solal (France), Gerasimos Filippatos (Greece), John J.V. McMurray (UK), Piotr Ponikowski (Poland), Philip Alexander Poole-Wilson (UK), Anna Strömberg (Sweden), Dirk J. van Veldhuisen (The Netherlands), Dan Atar (Norway), Arno W. Hoes (The Netherlands), Andre Keren (Israel), Alexandre Mebazaa (France), Markku Nieminen (Finland), Silvia Giuliana Priori (Italy), Karl Swedberg (Sweden)
Bridges are critical components of national infrastructure and smart cities. Therefore, smart bridge monitoring is essential for ensuring public safety and preventing catastrophic failures or accidents. Traditional bridge monitoring methods rely heavily on human visual inspections, which are time-consuming and prone to subjectivity and error. This paper proposes an artificial intelligence (AI)-driven anomaly detection approach for smart bridge monitoring. Specifically, a simple machine learning (ML) model is developed using real-time sensor data collected by the iBridge sensor devices installed on a bridge in Norway. The proposed model is evaluated against different ML models. Experimental results demonstrate that the density-based spatial clustering of applications with noise (DBSCAN)-based model outperforms in accurately detecting the anomalous events (bridge accident). These findings indicate that the proposed model is well-suited for smart bridge monitoring and can enhance public safety by enabling the timely detection of unforeseen incidents.
Inflation prediction guides decisions on interest rates, investments, and wages, playing a key role in economic stability. Yet accurate forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index, which organizes goods and services into multiple categories. We propose the Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model to address these challenges by leveraging the hierarchical structure to enable bidirectional information flow between levels. Informative constraints on the RNN parameters enhance predictive accuracy at all levels without the inefficiencies of a unified model. We validated BiHRNN on inflation datasets from the United States, Canada, and Norway by training, tuning hyperparameters, and experimenting with various loss functions. Our results demonstrate that BiHRNN significantly outperforms traditional RNN models, with its bidirectional architecture playing a pivotal role in achieving improved forecasting accuracy.
Önder Gürcan, Vanja Falck, Markus G. Rousseau
et al.
We present Social Digital Twinner, an innovative social simulation tool for exploring plausible effects of what-if scenarios in complex adaptive social systems. The architecture is composed of three seamlessly integrated parts: a data infrastructure featuring real-world data and a multi-dimensionally representative synthetic population of citizens, an LLM-enabled agent-based simulation engine, and a user interface that enable intuitive, natural language interactions with the simulation engine and the artificial agents (i.e. citizens). Social Digital Twinner facilitates real-time engagement and empowers stakeholders to collaboratively design, test, and refine intervention measures. The approach is promoting a data-driven and evidence-based approach to societal problem-solving. We demonstrate the tool's interactive capabilities by addressing the critical issue of youth school dropouts in Kragero, Norway, showcasing its ability to create and execute a dedicated social digital twin using natural language.
This article explores the factors that facilitate or constrain the co‐production of child welfare services (CWS) in the encounters between immigrant parents and child welfare systems. It draws on empirical data from interviews with ten parents who have experience with the Norwegian Child Welfare Services (NCWS) due to allegations of child maltreatment. The data were analyzed using reflexive thematic analysis, involving multiple iterative cycles and theme construction to identify factors that influence active parental participation in the process and, by extension, co‐production of the services. The findings reveal that while a range of factors shape the co‐production of CWS, they highlight the central role of: (a) parents’ negative perceptions of the NCWS and limited awareness of how to engage with the system; (b) the impact of the child welfare system’s approach to intervention; (c) the role of parental trust or distrust in the NCWS; and (d) the quality of relationships and the underlying power dynamics between parents and the NCWS. Yet, while some factors—such as parents’ negative perceptions and limited awareness—appear to have a more pronounced impact on specific stages of co‐production, like early engagement and collaborative planning, others, like trust and power dynamics, exert a crosscutting influence that shapes participation and co‐production across the full spectrum of the intervention process.
F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi, F. E. Kemgang Ghomsi
et al.
This study provides an in-depth evaluation of sea level rise (SLR) and its varied effects across the coastal regions of southern Africa. Utilizing data collected between 1993 and 2022, we analyze SLR patterns alongside land subsidence phenomena, based on observations from 10 strategically located tide gauges and X-TRACK satellite altimetry datasets. To ensure greater accuracy, the Coastal Altimetry Approach was adopted to refine nearshore measurements. Findings indicate that in areas such as Cape Town, sea-level rise rates reach around 6.3 mm/year, which is nearly twice the current global average of 3.3 mm/year. The interaction between rapid sea-level rise and subsidence rates surpassing 2.2 mm/year presents significant threats to coastal communities, critical infrastructure, and natural ecosystems. Moreover, the study highlights how seismic activity contributes to coastal dynamics, illustrating the role of earthquake-induced subsidence in magnifying the impacts of SLR. By incorporating seismic factors into the analysis, a more comprehensive understanding of the interplay between natural and human-induced drivers of sea-level variability is achieved. Additionally, the study examines the broader effects of SLR on Africa’s culturally and historically important coastal heritage sites, emphasizing the urgent need for proactive coastal management and climate adaptation efforts.
Farhad Rezazadeh, Shirin Aali, Fariborz Imani
et al.
<i>Background and Objectives:</i> Chronic low back pain (CLBP) is associated with altered neuromuscular control. Dynamic Neuromuscular Stabilization (DNS) targets core–limb coordination; however, its specific impact on lower-limb electromyographic (EMG) activity during gait remains unclear. <i>Materials and Methods:</i> Fifty-five young adults with non-specific CLBP (pain ≥ 3 months with no identifiable specific pathology) completed the trial (overall mean age 23.7 ± 1.3 years). Participants were randomized to an 8-week DNS program or a control. Pre-/Post-intervention surface EMG during gait and clinical outcomes (VAS, ODI) were assessed. <i>Results:</i> Compared with control, DNS showed lower adjusted Post-test VAS (3.08 ± 0.25 vs. 6.13 ± 0.24; <i>ηp</i><sup>2</sup> = 0.596) and ODI (15.73 ± 1.55% vs. 34.36 ± 1.52%; <i>ηp</i><sup>2</sup> = 0.579). Directionally, DNS was associated with phase-specific EMG modulation: tibialis anterior during mid-stance was lower (<i>ηp</i><sup>2</sup> = 0.137), rectus femoris during push-off was lower (<i>ηp</i><sup>2</sup> = 0.119), biceps femoris during push-off was lower (<i>ηp</i><sup>2</sup> = 0.168), and vastus medialis at heel-strike was higher (<i>ηp</i><sup>2</sup> = 0.077) relative to control. Other muscle–phase pairs showed no adjusted between-group differences. <i>Conclusions:</i> An 8-week DNS program was associated with clinically meaningful reductions in pain and disability and with phase-specific changes in lower-limb EMG during gait. These findings support DNS as a promising rehabilitation option for young adults with CLBP; confirmation in larger trials with active comparators is warranted.
Pierre Berthet, Beathe C Haatveit, Rikka Kjelkenes
et al.
Abstract Background Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms. Design Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms. Results LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores. Conclusions This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.
Costantino Carugno, Jake Muff, Mikael P. Johansson
et al.
This paper presents the Nordic-Estonian Quantum Computing e-Infrastructure Quest - NordIQuEst - an international collaboration of scientific and academic organizations from Denmark, Estonia, Finland, Norway, and Sweden, working together to develop a hybrid High-Performance and Quantum Computing (HPC+QC) infrastructure. The project leverages existing and upcoming classical high-performance computing and quantum computing systems, facilitating the development of interconnected systems. Our effort pioneers a forward-looking architecture for both hardware and software capabilities, representing an early-stage development in hybrid computing infrastructure. Here, we detail the outline of the initiative, summarizing the progress since the project outset, and describing the framework established. Moreover, we identify the crucial challenges encountered, and potential strategies employed to address them.
We construct flexible spatio-temporal models through stochastic partial differential equations (SPDEs) where both diffusion and advection can be spatially varying. Computations are done through a Gaussian Markov random field approximation of the solution of the SPDE, which is constructed through a finite volume method. The new flexible non-separable model is compared to a flexible separable model both for reconstruction and forecasting, and evaluated in terms of root mean square errors and continuous rank probability scores. A simulation study demonstrates that the non-separable model performs better when the data is simulated from a non-separable model with diffusion and advection. Further, we estimate surrogate models for emulating the output of a ocean model in Trondheimsfjorden, Norway, and simulate observations of autonomous underwater vehicles. The results show that the flexible non-separable model outperforms the flexible separable model for real-time prediction of unobserved locations.
Giuseppe Silano, Evangelos Rikos, Vetrivel Rajkumar
et al.
This paper investigates the integration and validation of multi-energy systems within the H2020 ERIGrid 2.0 project, focusing on the deployment of the JaNDER software middleware and universal API (uAPI) to establish a robust, high-data-rate, and low-latency communication link between Research Infrastructures (RIs). The middleware facilitates seamless integration of RIs through specifically designed transport layers, while the uAPI provides a simplified and standardized interface to ease deployment. A motivating case study explores the provision of power-to-heat services in a local multi-energy district, involving laboratories in Denmark, Greece, Italy, the Netherlands, and Norway, and analyzing their impact on electrical and thermal networks. This paper not only demonstrates the practical application of Geographically Distributed Simulations and Hardware-in-the-Loop technologies but also highlights their effectiveness in enhancing system flexibility and managing grid dynamics under various operational scenarios.
Yvonne Anne Michel, Eline Aas, Liv Ariane Augestad
et al.
Abstract Background Existing knowledge on healthcare use and costs in the last months of life is often limited to one patient group (i.e., cancer patients) and one level of healthcare (i.e., secondary care). Consequently, decision-makers lack knowledge in order to make informed decisions about the allocation of healthcare resources for all patients. Our aim is to elaborate the understanding of resource use and costs in the last six months of life by describing healthcare use and costs for all causes of death and by all levels of formal care. Method Using five national registers, we gained access to patient-level data for all individuals who died in Norway between 2009 and 2013. We described healthcare use and costs for all levels of formal care—namely primary, secondary, and home- and community-based care —in the last six months of life, both in total and differentiated across three time periods (6-4 months, 3-2 months, and 1-month before death). Our analysis covers all causes of death categorized in ten ICD-10 categories. Results During their last six months of life, individuals used an average of healthcare resources equivalent to €46,000, ranging from €32,000 (Injuries) to €64,000 (Diseases of the nervous system and sense organs). In terms of care level, 63% of healthcare resources were used in home- and community-based care (i.e., in-home nursing, practical assistance, or nursing home care), 35% in secondary care (mostly hospital care), and 2% in primary care (i.e., general practitioners). The amount and level of care varied by cause of death and by time to death. The proportion of home- and community-based care which individuals received during their last six months of life varied from 38% for cancer patients to 92% for individuals dying with mental diseases. The shorter the time to death, the more resources were needed: nearly 40% of all end-of-life healthcare costs were expended in the last month of life across all causes of death. The composition of care also differed depending on age. Individuals aged 80 years and older used more home- and community-based care (77%) than individuals dying at younger ages (40%) and less secondary care (old: 21% versus young: 57%). Conclusions Our analysis provides valuable evidence on how much healthcare individuals receive in their last six months of life and the associated costs, broken down by level of care and cause of death. Healthcare use and costs varied considerably by cause of death, but were generally higher the closer a person was to death. Our findings enable decision-makers to make more informed resource-allocation decisions and healthcare planners to better anticipate future healthcare needs.
Løseth Sissel, Nebuchennykh Maria, Brokstad Ruth Therese
et al.
We aimed to investigate to what extent small fiber tests were abnormal in an unselected retrospective patient material with symptoms suggesting that small fiber neuropathy (SFN) could be present, and to evaluate possible gender differences.
Special situations and conditions, Medicine (General)
Anna Rytivaara, Raisa Ahtiainen, Iines Palmu
et al.
Research on how teachers learn to co-teach is scarce. In this systematic review, the PRISMA method was used to examine the relationship between teacher learning and co-teaching in professional development programmes. Inclusion criteria was used to identify 567 articles on K–12 co-teaching, published in 2009–2018. A detailed analysis of nine articles revealed that the linkage between co-teaching and teacher learning remained narrow. Various programmes showed that the existing understanding of co-teaching or teacher learning was not used efficiently. Considerable variation in the programmes regarding the concepts, methods, and practices highlight the importance of conducting future research.
COVID-19 pandemic has irreversibly changed the attitude towards office presence. While previously remote workers were met with skepticism and distrust, today the same applies to companies prohibiting remote working. Albeit many workspaces are half empty. In this paper, we offer insights into the role of the office, corporate policies and actions regarding remote work in eight companies: Ericsson, Knowit, SpareBank 1 Utvikling, Spotify, Storebrand, Telenor, Company-X, Company-Y, and their sites in Sweden, Norway and the UK. Our findings are twofold. First, we found that companies indeed struggle with office presence and a large share of corporate space (35-67%) is underutilized. Second, we found that the main motivator for office presence is Connection and community, followed by Material offerings, Preference and Duty. Finally, we summarize actionable advice to promote onsite work, which is likely to help many other companies to rejuvenate life in their offices.
Maciej Wielgosz, Stefano Puliti, Phil Wilkes
et al.
This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in Våler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.
Isotropic covariance structures can be unreasonable for phenomena in three-dimensional spaces such as the ocean. In the ocean, the variability of the response may vary with depth, and ocean currents may lead to spatially varying anisotropy. We construct a class of non-stationary anisotropic Gaussian random fields (GRFs) in three dimensions through stochastic partial differential equations (SPDEs) where computations are done using Gaussian Markov random field approximations. The approach is proven in a simulation study where the amount of data required to estimate these models is explored. Then, the method is applied to construct a GRF prior on an ocean mass outside Trondheim, Norway, based on simulations from the complex numerical ocean model SINMOD. This GRF prior is compared to a stationary anisotropic GRF using in-situ measurements collected with an autonomous underwater vehicle where our approach outperforms the stationary anisotropic GRF for real-time prediction of unobserved locations.