Hasil untuk "Norway"

Marco Spanghero, Filip Geib, Ronny Panier et al.

Global Navigation Satellite Systems (GNSS) are fundamental in ubiquitously providing position and time to a wide gamut of systems. Jamming remains a realistic threat in many deployment settings, civilian and tactical. Specifically, in Unmanned Aerial Vehicles (UAVs) sustained denial raises safety critical concerns. This work presents a strategy that allows detection, localization, and classification both in the frequency and time domain of interference signals harmful to navigation. A high-performance Vertical Take Off and Landing (VTOL) UAV with a single antenna and a commercial GNSS receiver is used to geolocate and characterize RF emitters at long range, to infer the navigation impairment. Raw IQ baseband snapshots from the GNSS receiver make the application of spectral correlation methods possible without extra software-defined radio payload, paving the way to spectrum identification and monitoring in airborne platforms, aiming at RF situational awareness. Live testing at Jammertest, in Norway, with portable, commercially available GNSS multi-band jammers demonstrates the ability to detect, localize, and characterize harmful interference. Our system pinpointed the position with an error of a few meters of the transmitter and the extent of the affected area at long range, without entering the denied zone. Additionally, further spectral content extraction is used to accurately identify the jammer frequency, bandwidth, and modulation scheme based on spectral correlation techniques.

Yash Agrawal, Saurabh Singh, Girish B. S. et al.

Radio Frequency Interference (RFI) presents a significant challenge for carrying out precision measurements in radio astronomy. In particular, RFI can be a showstopper when looking for faint cosmological signals such as the red-shifted 21-cm line from cosmic dawn (CD) and epoch of reionization (EoR). As wireless communications, satellite transmissions, and other RF technologies proliferate globally, understanding the RFI landscape has become essential for site selection and data integrity. We present findings from RFI surveys conducted at four distinct locations: three locations in India, the Gauribidanur Radio Observatory in Karnataka, Twin Lakes in Ladakh, Kalpong Dam in the Andaman Islands, and the Gruvebadet Atmosphere Laboratory in Ny-Ålesund, Svalbard, Norway. These sites, selected based on their geographical diversity and varying levels of human activity, were studied to assess RFI presence in 30-300 MHz bands, critical for low-frequency observations and experiments targeting the 21-cm CD/EoR signal. Using an automated RFI detection approach via the Hampel filter and singular value decomposition, the surveys identified both persistent and transient interference, which varies with location and time. The results provide a comprehensive view of the RFI environment at each site, informing the feasibility of long-term cosmological observations and aiding in the mitigation of RFI in radio astronomical data. The methods developed to characterize RFI can be easily generalized to any location and experiment.

Widar Weizhi Wang, Konstantinos Christakos, Csaba Pakozdi et al.

Offshore wave studies often assume Gaussian processes and homogeneous wave fields. However, as waves approach the shoreline, complex coastal topo-bathymetry induces transformations such as shoaling, refraction, diffraction, reflection, and breaking, leading to increased nonlinearity and site-specific wave characteristics. This complexity necessitates detailed site-specific studies for coastal infrastructure design and blue economy planning. This work presents a downscaling procedure for analyzing wave-structure interactions from offshore metocean conditions. The open-access NORA3 and NORA10EI hindcast databases define offshore sea states, which are propagated to nearshore regions using the phase-averaged wave model SWAN. The outputs inform phase-resolving simulations with the fully nonlinear potential flow solver REEF3D::FNPF, incorporating an Arbitrary Eulerian-Lagrangian (ALE) method to compute wave forces via Morisons formulation and to screen for extreme events. Extreme wave loads are further examined using the fully viscous Navier-Stokes solver REEF3D::CFD. A one-way hydrodynamic coupling (HDC) between the potential flow and viscous solvers ensures accurate information transfer. The proposed NORA-SARAH framework, integrating NORA databases with SWAN, REEF3D, ALE, and HDC, offers a robust approach for complex coastal environments. A case study in Southern Norway demonstrates its advantages over traditional significant wave height (Hs)-based or phase-averaged modeling practices, highlighting the necessity of this downscaling method.

Felix Simon Reimers, Carl-Hendrik Peters, Stefano Nichele

Using data from mobile network utilization in Norway, we showcase the possibility of monitoring the state of communication and mobility networks with a non-invasive, low-cost method. This method transforms the network data into a model within the framework of reservoir computing and then measures the model's performance on proxy tasks. Experimentally, we show how the performance on these proxies relates to the state of the network. A key advantage of this approach is that it uses readily available data sets and leverages the reservoir computing framework for an inexpensive and largely agnostic method. Data from mobile network utilization is available in an anonymous, aggregated form with multiple snapshots per day. This data can be treated like a weighted network. Reservoir computing allows the use of weighted, but untrained networks as a machine learning tool. The network, initialized as a so-called echo state network (ESN), projects incoming signals into a higher dimensional space, on which a single trained layer operates. This consumes less energy than deep neural networks in which every weight of the network is trained. We use neuroscience inspired tasks and trained our ESN model to solve them. We then show how the performance depends on certain network configurations and also how it visibly decreases when perturbing the network. While this work serves as proof of concept, we believe it can be elevated to be used for near-real-time monitoring as well as the identification of possible weak spots of both mobile communication networks as well as transportation networks.

Johanne Rauwenhoff, Gøril Storvig, Bert Lenaert et al.

A substantial number of people experience persistent post-concussion symptoms (PPCS) following a concussion. Traditional retrospective assessments, such as the Rivermead Post Concussion Symptoms Questionnaire (RPQ), are prone to memory biases and do not capture the day-to-day variability of PPCS. In this study, we explored the association between daily registrations of PPCS and the RPQ. We also examined the variability of PPCS trajectories over time. Nineteen participants registered PPCS symptoms for 28 days using an mHealth app and then completed the RPQ. From the final 7 days, average, highest, and last-day symptom scores were calculated and correlated with corresponding RPQ items. Scores from the full 28-day period were used to compute the within-person standard deviation and mean squared successive difference (MSSD) for each symptom that participants rated as the most bothersome. Correlations between the RPQ and daily registrations were weak-to-medium (range: 0.343, 0.590). The retrospective RPQ explained up to 35% of the variance in average daily registrations of PPCS. The MSSD ranged from 0 to 16.29, and the within-person SD from 0 to 3.25. Visual analyses showed that participants with identical RPQ item scores often exhibited different PPCS variability. This was also true for different symptoms within the same participant. This study highlights the potential additional value of daily registrations for capturing the dynamic and fluctuating nature of PPCS, which may be missed by retrospective questionnaires administered at one time point. PPCS vary both within and between individuals over time and reducing this complexity to a single total score oversimplifies a nuanced reality. Larger studies are needed to confirm these findings, and future work should investigate the clinical relevance of capturing daily variations in PPCS.

Alfonso J Carrillo, María Balaguer, Cecilia Solís et al.

Nanoparticle exsolution is a powerful technique for functionalizing redox oxides in energy applications, particularly at high temperatures. It shows promise for solid oxide fuel cells and electrolyzers. However, exsolution of other chemistries like metal oxides is not well studied, and the mechanism is poorly understood. This work explores oxide exsolution in PrBa _1− _x Co _2 O _6− _δ ( x = 0, 0.05, 0.1, 0.15) double perovskites, practiced electrodes in proton ceramic fuel cells and electrolyzers. Oxide exsolution in PrBa _1− _x Co _2 O _6− _δ aimed at boosting the electrocatalytic activity and was evaluated by varying intrinsic materials-related properties, viz. A-site deficiency and external parameters (temperature, under fixed time, and p O _2 = 10 ^−5 atm conditions). The materials were analyzed with conventional characterization tools and synchrotron-based small-angle x-ray scattering. Unlike metal-nanoparticle exsolution, increasing the A-site deficiency did not enhance the extent of oxide-nanoparticle exsolution, whereas larger nanoparticles were obtained by increasing the exsolution temperature. Combined Raman spectroscopy and electron microscopy analysis revealed that BaCoO _3 , Co _3 O _4 , and amorphous BaCO _3 nanoparticles were formed on the surface of the double perovskites after the reductive treatments. The present results demonstrate the complexity of oxide-nanoparticle exsolution in comparison with metal-nanoparticle exsolution. Further materials screening and mechanistic studies are needed to enhance our understanding of this method for functionalizing proton ceramic electrochemical cells (PCEC) electrodes.

Margrete Haram, Rune Hansen, Ola Finneng Myhre et al.

Objective: Preclinical trials have demonstrated promising results for increased tumor uptake and therapeutic effect of drugs combined with focused ultrasound (FUS) and microbubbles (MBs). The aim of this clinical trial was to investigate whether FUS and MB could improve the effect of chemotherapy in patients with inoperable pancreatic ductal adenocarcinoma (PDAC) and to investigate safety and feasibility. Methods: Twenty patients with inoperable PDAC were included and randomized for either chemotherapy combined with FUS and MBs or only chemotherapy, but one patient in the FUS and MB group had later to be excluded. A new dual-frequency transducer for imaging (4.5 MHz) and treatment (0.35 MHz) was used. After chemotherapy (FOLFIRINOX or nab-paclitaxel-gemcitabine), the treatment group was exposed to FUS (frequency 0.35 MHz, mechanical index 0.5, pulse length 2.9 ms (1000 cycles), pressure amplitude 0.3 MPa and MBs (SonoVue) for 35 min). Nine boluses of MBs were injected intravenously (i.v) with a 3.5 min interval. Patients were scheduled for two months of treatment. Changes in the size of tumors were determined from Computed Tomography (CT) -images. Results: Treatment with FUS and MB is safe with the used settings. No additional effects of FUS and MBs regarding tumor volume or resectability were observed. Overall survival increased, from 9.8 months to 11.7 months for the patients receiving FUS ​+ ​MB, although not statistically significant. Conclusion: FUS combined with MBs is a safe, feasible, and available strategy for potentially improving the effect of chemotherapy in pancreatic cancer patients. Therapeutic effect was not demonstrated in this trial. Reducing the time between chemotherapy and injection of MB and FUS and optimization of ultrasound parameters may improve the treatment effect. Multicenter trials with standardized protocols should be performed.

Abedelaziz Khalil, Brett Bligh

This study explores the significance of Open Educational Practices (OEP) in higher education, emphasizing its role in fostering collaboration, diversity, and knowledge sharing. Prior research has failed to emphasize how experienced practitioners understand OEP. Adopting a phenomenographic approach, this study analyses the perceptions of seven educators and six administrators from an Open University in Palestine about OEP. The analysis reveals four distinct and progressively include ways in which these practitioners understand OEP: (1) Recontextualizing open resources and methods; (2) Collaborating and engaging learners through pragmatic activities; (3) Empowering practitioners by enhancing their understanding of openness; and (4) Engaging communities through knowledge creation and exchange. This progression highlights a shift from viewing OEP as a classroom-based practice towards a global community engagement effort. The study contributes to the literature by expanding the meaning of OEP beyond content to include open pedagogies and collaborative practices. It offers a novel perspective on OEP implementation, emphasizing interconnected open platforms for immersive learning. Additionally, it provides insights into the evolving roles of OEP practitioners, seen as recruiters of other practitioners, thereby extending OEP to a broader community.

Ulrik Langen - Frederik Stjernfelt

Erblin Isaku, Christoph Laaber, Hassan Sartaj et al.

The Cancer Registry of Norway (CRN) uses an automated cancer registration support system (CaReSS) to support core cancer registry activities, i.e, data capture, data curation, and producing data products and statistics for various stakeholders. GURI is a core component of CaReSS, which is responsible for validating incoming data with medical rules. Such medical rules are manually implemented by medical experts based on medical standards, regulations, and research. Since large language models (LLMs) have been trained on a large amount of public information, including these documents, they can be employed to generate tests for GURI. Thus, we propose an LLM-based test generation and differential testing approach (LLMeDiff) to test GURI. We experimented with four different LLMs, two medical rule engine implementations, and 58 real medical rules to investigate the hallucination, success, time efficiency, and robustness of the LLMs to generate tests, and these tests' ability to find potential issues in GURI. Our results showed that GPT-3.5 hallucinates the least, is the most successful, and is generally the most robust; however, it has the worst time efficiency. Our differential testing revealed 22 medical rules where implementation inconsistencies were discovered (e.g., regarding handling rule versions). Finally, we provide insights for practitioners and researchers based on the results.

Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor

Representation learning for time series using contrastive learning has emerged as a critical technique for improving the performance of downstream tasks. To advance this effective approach, we introduce CaTT (\textit{Contrast All The Time}), a new approach to unsupervised contrastive learning for time series, which takes advantage of dynamics between temporally similar moments more efficiently and effectively than existing methods. CaTT departs from conventional time-series contrastive approaches that rely on data augmentations or selected views. Instead, it uses the full temporal dimension by contrasting all time steps in parallel. This is made possible by a scalable NT-pair formulation, which extends the classic N-pair loss across both batch and temporal dimensions, making the learning process end-to-end and more efficient. CaTT learns directly from the natural structure of temporal data, using repeated or adjacent time steps as implicit supervision, without the need for pair selection heuristics. We demonstrate that this approach produces superior embeddings which allow better performance in downstream tasks. Additionally, training is faster than other contrastive learning approaches, making it suitable for large-scale and real-world time series applications. The source code is publicly available at \href{https://github.com/sfi-norwai/CaTT}{https://github.com/sfi-norwai/CaTT}.

Khanh Truong, Jo Eidsvik, Robin Andre Rørstadbotnen

Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tracking vehicles using DAS data, focusing on real-time processing through edge computing. Our approach applies the Hough transform to detect straight-line segments in the spatiotemporal DAS data, corresponding to vehicles crossing the Astfjord bridge in Norway. These segments are further clustered using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm to consolidate multiple detections of the same vehicle, reducing noise and improving accuracy. The proposed workflow effectively counts vehicles and estimates their speed with only tens of seconds latency, enabling real-time traffic monitoring on the edge. To validate the system, we compare DAS data with simultaneous video footage, achieving high accuracy in vehicle detection, including the distinction between cars and trucks based on signal strength and frequency content. Results show that the system is capable of processing large volumes of data efficiently. We also analyze vehicle speeds and traffic patterns, identifying temporal trends and variations in traffic flow. Real-time deployment on edge devices allows immediate analysis and visualization via cloud-based platforms. In addition to traffic monitoring, the method successfully detected structural responses in the bridge, highlighting its potential use in structural health monitoring.

Nicola Karcher, Kjetil Braut Simonsen

This special issue examines conspiracist antisemitic print culture in the Nordic countries from the Bolshevik Revolution of 1917 to the defeat of Nazi Germany in 1945. To contrast the universal patterns and particularities of the cases of Finland, Sweden, Denmark and Norway, the issue includes two contributions analysing Spain and Britain. Together, the articles provide empirical in-depth knowledge of the character and dissemination of conspiracist antisemitism in a particular time and within a particular region. Our aim is to expand the general knowledge of conspiracism as a historical phenomenon through the prism of antisemitism. In the introduction, we present the conditions of historical antisemitism in each case study as well as the conceptual framework of this issue, focusing on terms such as conspiracism, conspiracy and conspiracy theories. We argue that antisemitism can be interpreted as a longue-durée conspiracist tradition, marked by a dialectic interaction between continuity and dynamic changes.

Cristina Palmero, Mikel deVelasco, Mohamed Amine Hmani et al.

The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. One of the core aspects of the system is its human sensing capabilities, allowing for the perception of emotional states to provide a personalized experience. This paper outlines the development of the emotion expression recognition module of the virtual coach, encompassing data collection, annotation design, and a first methodological approach, all tailored to the project requirements. With the latter, we investigate the role of various modalities, individually and combined, for discrete emotion expression recognition in this context: speech from audio, and facial expressions, gaze, and head dynamics from video. The collected corpus includes users from Spain, France, and Norway, and was annotated separately for the audio and video channels with distinct emotional labels, allowing for a performance comparison across cultures and label types. Results confirm the informative power of the modalities studied for the emotional categories considered, with multimodal methods generally outperforming others (around 68% accuracy with audio labels and 72-74% with video labels). The findings are expected to contribute to the limited literature on emotion recognition applied to older adults in conversational human-machine interaction.

Stefan Andersson, Stefan Andersson, David Gobrecht et al.

Reactions of SiO molecules have been postulated to initiate efficient formation of silicate dust particles in outflows around dying (AGB) stars. Both OH radicals and H2O molecules can be present in these environments and their reactions with SiO and the smallest SiO cluster, Si2O2, affect the efficiency of eventual dust formation. Rate coefficients of gas-phase oxidation and clustering reactions of SiO, Si2O2 and Si2O3 have been calculated using master equation calculations based on density functional theory calculations. The calculations show that the reactions involving OH are fast. Reactions involving H2O are not efficient routes to oxidation but may under the right conditions lead to hydroxylated species. The reaction of Si2O2 with H2O, which has been suggested as efficient producing Si2O3, is therefore not as efficient as previously thought. If H2O molecules dissociate to form OH radicals, oxidation of SiO and dust formation could be accelerated. Kinetics simulations of oxygen-rich circumstellar environments using our proposed reaction scheme suggest that under typical conditions only small amounts of SiO2 and Si2O2 are formed and that most of the silicon remains as molecular SiO.

Ulla-Britt Wennerholm, Ulla-Britt Wennerholm, Lina Bergman et al.

BackgroundPreterm birth is the leading cause of childhood mortality and morbidity. We aimed to provide a comprehensive systematic review on randomized controlled trials (RCTs) on progesterone, cerclage, pessary, and acetylsalicylic acid (ASA) to prevent preterm birth in asymptomatic women with singleton pregnancies defined as risk of preterm birth and multifetal pregnancies.MethodsSix databases (including PubMed, Embase, Medline, the Cochrane Library) were searched up to February 2022. RCTs published in English or Scandinavian languages were included through a consensus process. Abstracts and duplicates were excluded. The trials were critically appraised by pairs of reviewers. The Cochrane risk-of-bias tool was used for risk of bias assessment. Predefined outcomes including preterm birth, perinatal/neonatal/maternal mortality and morbidity, were pooled in meta-analyses using RevMan 5.4, stratified for high and low risk of bias trials. The certainty of evidence was assessed using the GRADE approach. The systematic review followed the PRISMA guideline.ResultsThe search identified 2,309 articles, of which 87 were included in the assessment: 71 original RCTs and 16 secondary publications with 23,886 women and 32,893 offspring. Conclusions were based solely on trials with low risk of bias (n = 50). Singleton pregnancies: Progesterone compared with placebo, reduced the risk of preterm birth <37 weeks 26.7% vs. 30.3% [risk ratio (RR) 0.82 (95% confidence interval [CI] 0.71–0.96)] (high certainty of evidence, 13 trials) thereby reducing neonatal mortality and respiratory distress syndrome. Cerclage probably reduced the risk of preterm birth <37 gestational weeks: 29.0% vs. 37.6% (RR 0.78 [95% CI 0.69 to 0.88]) (moderate certainty of evidence, four open trials). In addition, perinatal mortality may be reduced by cerclage. Pessary did not demonstrate any overall effect. ASA did not affect any outcome, but evidence was based on one underpowered study. Multifetal pregnancies: The effect of progesterone, cerclage, or pessary was minimal, if any. No study supported improved long-term outcome of the children.ConclusionProgesterone and probably also cerclage have a protective effect against preterm birth in asymptomatic women with a singleton pregnancy at risk of preterm birth. Further trials of ASA are needed. Prevention of preterm birth requires screening programs to identify women at risk of preterm birth.Systematic Review Registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42021234946].

Johan Bijnens, Nils Hermansson-Truedsson, Antonio Rodríguez-Sánchez

The muon anomalous magnetic moment continues to attract interest due to the potential tension between experimental measurement [1,2] and the Standard Model prediction [3]. The hadronic light-by-light contribution to the magnetic moment is one of the two diagrammatic topologies currently saturating the theoretical uncertainty. With the aim of improving precision on the hadronic light-by-light in a data-driven approach founded on dispersion theory [4,5], we derive various short-distance constraints of the underlying correlation function of four electromagnetic currents. Here, we present our previous progress in the purely short-distance regime and current efforts in the so-called Melnikov-Vainshtein limit.

Tobias Engelhardt Rasmussen, Line H Clemmensen, Andreas Baum

Convolutional Neural Networks (CNN) are the state-of-the-art in the field of visual computing. However, a major problem with CNNs is the large number of floating point operations (FLOPs) required to perform convolutions for large inputs. When considering the application of CNNs to video data, convolutional filters become even more complex due to the extra temporal dimension. This leads to problems when respective applications are to be deployed on mobile devices, such as smart phones, tablets, micro-controllers or similar, indicating less computational power. Kim et al. (2016) proposed using a Tucker-decomposition to compress the convolutional kernel of a pre-trained network for images in order to reduce the complexity of the network, i.e. the number of FLOPs. In this paper, we generalize the aforementioned method for application to videos (and other 3D signals) and evaluate the proposed method on a modified version of the THETIS data set, which contains videos of individuals performing tennis shots. We show that the compressed network reaches comparable accuracy, while indicating a memory compression by a factor of 51. However, the actual computational speed-up (factor 1.4) does not meet our theoretically derived expectation (factor 6).

Stephan Wüstenhagen, Daniella Terney, Elena Gardella et al.

Objective: To determine the prevalence and characteristics of normal variants in EEG recordings in a large cohort, and provide readers with typical examples of all normal variants for educational purposes. Methods: Using the SCORE EEG system (Standardized Computer-Based Organized Reporting of EEG), we prospectively extracted EEG features in consecutive patients. In this dataset, we analyzed 3050 recordings from 2319 patients (mean age 38.5 years; range: 1–89 years). Results: The distribution of the normal variants was as follows: sharp transients 19.21% (including wicket spikes), rhythmic temporal theta of drowsiness 6.03%, temporal slowing of the old 2.89%, slow fused transients 2.59%, 14-and 6-Hz bursts 1.83%, breach rhythm 1.25%, small sharp spikes 1.05%, 6-Hz spike and slow wave 0.69% and SREDA 0.03%. Conclusions: The most prevalent normal variants are the sharp transients, which must not be over-read as epileptiform discharges. Significance: EEG readers must be familiar with the normal variants to avoid misdiagnosis and misclassification of patients referred to clinical EEG recordings.

Christine Sundgot-Borgen, Lise Katrine Jepsen Trangsrud, Tobias Otterbring et al.

Plain English summary Physical activity helps us feel good about ourselves and appreciate our bodies. However, less is known about the extent to which different arenas for physical activity are related to body appreciation. Therefore, 360 adults from Norway completed a survey with questions about their physical activity level, use of nature, fitness centers, and organized sports as arenas for physical activity, and measures linked to connectedness with nature, self-compassion, and body appreciation. We found an association between body appreciation and performing physical activity at fitness centers and in nature. Physical activity at these two arenas together with self-compassion and connectedness with nature explained body appreciation in these adults. Future studies should focus on the adolescent population, where the level of body appreciation tend to be lower and where organized sports, in addition to fitness centers, constitute an even more commonly used physical activity arena.