Hasil untuk "Norway"

Liudong Chen, Hyemi Kim, Adam N. Elmachtoub et al.

A virtual power plant (VPP) is operated by an aggregator that acts as a market intermediary, aggregating consumers to participate in wholesale power markets. By setting incentive prices, the aggregator induces consumers to sell energy and profits by providing this aggregated energy to the market. This supply is enabled by consumers' flexibility to adjust electricity consumption in response to market conditions. However, heterogeneity in flexibility means that profit-maximizing VPP pricing can create inequalities in participation and benefit allocation across consumers. In this paper, we develop a fairness-aware pricing framework to analyze how different fairness notions reshape system performance, measured by consumer Nash welfare, total consumer utility, and social welfare. We consider three fairness criteria: energy fairness, which ensures equitable energy provision; price fairness, which ensures similar incentive prices; and utility fairness, which ensures comparable levels of consumer utility. We model the aggregator-consumer interaction as a Stackelberg game and derive consumers' optimal responses to incentive prices. Using a stylized model, we show that profit-only pricing systematically disadvantages less flexible consumers. We further show that energy fairness can either improve or worsen all performance measures, and gains across most measures arise only at moderate fairness levels. Surprisingly, price fairness never benefits less flexible consumers, even when it reduces price disparities. By contrast, utility fairness protects less flexible consumers without benefiting more flexible ones. We validate our findings using data from an experiment in Norway under a tiered pricing scheme. Our results provide regulators and VPP operators with a systematic map linking fairness definitions and enforcement levels to operational and welfare outcomes.

Anne Guro Korsvoll

Hensikten med dette essayet er å gje nye blikk og vinklingar på samarbeid som grunnleggande kompetanse i sjukepleieprofesjonen. Samarbeid er noko me alle har eit forhold til, og ein kan få inntrykk av at samarbeidskompetanse er ein gjengangar i allmenn opplæring og høgare utdanning. Som høgskulelektor og sjukepleiar er eg dagleg vitne til, og del av, nettopp samarbeid, og gjennom essayet søker eg ei større forståing av kompleksiteten til denne klassiske arbeidsforma. Kva faktorar speler inn og ligg til grunn for å fremje synergi i samarbeid? Essayet tek utgangspunkt i sjukepleiestudenten, og eg inviterer lesaren med på mine refleksjonar, vinklingar og assosiasjonar knytt til samarbeidskompetanse. Litteraturen er henta frå eit utval filosofisk, pedagogisk og sjukepleiefagleg teori i tillegg til studie- og emneplan og forskrift om nasjonal retningslinje for sjukepleiarutdanning.  

Kevin H. Knuth, Philippe Ailleris, Hussein Ali Agrama et al.

After decades of dismissal and secrecy, it has become clear that a significant number of the world's governments take Unidentified Aerospace-Undersea Phenomena (UAP), formerly known as Unidentified Flying Objects (UFOs), seriously -- yet still seem to know little about them. As a result, these phenomena are increasingly attracting the attention of scientists around the world, some of whom have recently formed research efforts to monitor and scientifically study UAP. In this paper, we review and summarize approximately 20 historical government studies dating from 1933 to the present (in Scandinavia, WWII, US, Canada, France, Russia, China), several historical private research studies (France, UK, US), and both recent and current scientific research efforts (Ireland, Germany, Norway, Sweden, US). In doing so, our objective is to clarify the existing global and historical scientific narrative around UAP. Studies range from field station development and deployment to the collection and analysis of witness reports from around the world. We dispel the common misconception that UAPs are an American phenomenon and show that UAP can be, and have been, scientifically investigated. Our aim here is to enable future studies to draw on the great depth of prior documented experience.

Adrian Straker, Paul Magdon, Marco Zullich et al.

Aiming to advance research in the field of interpretability of deep learning models for tree species classification using TLS 3D point clouds we present insights in the classification abilities of YOLOv8 through a new framework which enables systematic analysis of saliency maps derived from CAM (Class Activation Mapping). To investigate the contribution of structural tree features to the classification decisions of the models, we link regions with high saliency derived from the application of Finer-CAM to segments of 2D side-view images that correspond to structural tree features. Using TLS 3D point clouds from 2445 trees across seven European tree species, we trained five YOLOv8 models with cross-validation, reaching a mean accuracy of 96% (SD = 0.24%) when applied to the test data. Our results demonstrate that Finer-CAM can be considered faithful in identifying discriminative regions that discriminate target tree species. This renders Finer-CAM suitable for enhancing the interpretability of the tree species classification models. Analysis of 630 saliency maps indicate that the models primarily rely on image regions associated with tree crowns for species classification. While this result is pronounced in Silver Birch, European Beech, English oak, and Norway Spruce, image regions associated with stems contribute more frequently to the differentiation of European ash, Scots pine, and Douglas-fir. We demonstrate that the visibility of detailed structural tree features in the 2D side-view images enhances the discriminative performances of the models, indicating YOLOv8`s abilities to leverage detailed point cloud representations. Our results represent a first step toward enhancing the understanding of the classification decision processes of tree species classification models, aiding in the identification of data set and model limitations, and building confidence in model predictions.

Eva B. Aamodt, Dag Alnæs, Ann Marie de Lange et al.

Introduction: Advanced age is associated with poorer prognosis after stroke. Machine learning based on brain imaging can be used to estimate the age of a patient and compute the difference to chronological age; the brain age gap (BAG). Higher BAG (an older appearing brain) has been reported in a range of clinical conditions and is associated with risk of dementia and cognitive decline following stroke, but the associations and predictive value of brain age prediction for post- stroke decline in specific cognitive domains remain unknown. To this end, using longitudinal data after stroke we tested the hypothesis that higher BAG during the acute stroke phase is associated with decline in specific cognitive domains 3 and 18 months after stroke. Methods: 272 stroke survivors (age = 71.1 (11.2), women = 43.4%) were included from the ‘Norwegian Cognitive Impairment After Stroke (Nor-COAST) study. Clinical and MRI data was collected in the acute phase of the stroke and cognitive assessment of attention, executive function, language, and memory was collected at 3 and 18-months follow-up. FreeSurfer anatomical segmentation machine learning was used to predict age of each patient. Z-scores of the cognitive data normalized by mean and standard deviation of published normative data were calculated. Results: Mean (SD) z-scores at 3 and 18 months respectively were, for attention; -.8 (2.5) and -.4 (2.2), for executive function; -.6 (1.4) and -4. (1.4), for language; -.6 (1.2) and -.3 (1.5), and for memory; -.8 (1.4) and -.8 (1.2). Generalised linear model analyses revealed significant associations between baseline BAG and all of the cognitive domains at both 3 and 18 months, with low/moderate effect sizes (see figure). Discussion: We found that higher BAG during the acute phase of a stroke is associated with poorer 3- and 18-month cognitive outcome in attention, executive function, language, and memory.These findings suggest that BAG may be used as a predictive marker for decline in specific cognitive domains after stroke.

Sushant Gautam

Fact-checking is a crucial natural language processing (NLP) task that verifies the truthfulness of claims by considering reliable evidence. Traditional methods are often limited by labour-intensive data curation and rule-based approaches. In this paper, we present FactGenius, a novel method that enhances fact-checking by combining zero-shot prompting of large language models (LLMs) with fuzzy text matching on knowledge graphs (KGs). Leveraging DBpedia, a structured linked data dataset derived from Wikipedia, FactGenius refines LLM-generated connections using similarity measures to ensure accuracy. The evaluation of FactGenius on the FactKG, a benchmark dataset for fact verification, demonstrates that it significantly outperforms existing baselines, particularly when fine-tuning RoBERTa as a classifier. The two-stage approach of filtering and validating connections proves crucial, achieving superior performance across various reasoning types and establishing FactGenius as a promising tool for robust fact-checking. The code and materials are available at https://github.com/SushantGautam/FactGenius.

Zhihao Liu, Simon Filhol, Désirée Treichler

Estimating the variability of seasonal snow cover, in particular snow depth in remote areas, poses significant challenges due to limited spatial and temporal data availability. This study uses snow depth measurements from the ICESat-2 satellite laser altimeter, which are sparse in both space and time, and incorporates them with climate reanalysis data into a downscaling-calibration scheme to produce monthly gridded snow depth maps at microscale (10 m). Snow surface elevation measurements from ICESat-2 along profiles are compared to a digital elevation model to determine snow depth at each point. To efficiently turn sparse measurements into snow depth maps, a regression model is fitted to establish a relationship between the retrieved snow depth and the corresponding ERA5 Land snow depth. This relationship, referred to as subgrid variability, is then applied to downscale the monthly ERA5 Land snow depth data. The method can provide timeseries of monthly snow depth maps for the entire ERA5 time range (since 1950). The validation of downscaled snow depth data was performed at an intermediate scale (100 m x 500 m) using datasets from airborne laser scanning (ALS) in the Hardangervidda region of southern Norway. Results show that snow depth prediction achieved R2 values ranging from 0.74 to 0.88 (post-calibration). The method relies on globally available data and is applicable to other snow regions above the treeline. Though requiring area-specific calibration, our approach has the potential to provide snow depth maps in areas where no such data exist and can be used to extrapolate existing snow surveys in time and over larger areas. With this, it can offer valuable input data for hydrological, ecological or permafrost modeling tasks.

Tohr Nilsson, Jens Wahlström, Eirik Reierth et al.

This systematic review covering publications in the Medline and Embase databases for the period 1946 to 2020 revealed a higher prevalence of Dupuytren’s disease among men exposed to vibration compared to men not exposed to vibration. The risk assessment, also considering the risk of bias, corresponded to a roughly doubled risk of Dupuytren’s disease when working with vibrating machines. The supplementary meta-analysis confirmed a more than doubled risk. A possible exposure–response relation was supported by the result from the meta-analysis, which showed a doubled risk for high exposure relative to low exposure.

Paulina Tedesco, Jean Rabault, Martin Lilleeng Sætra et al.

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute produces 120-hour regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS), using a model setup called Nordic4-SS. Despite advances in the development of models and computational capabilities, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation. Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict the residuals in forecasts from Nordic4-SS. A simple error mapping technique and a more sophisticated Neural Network (NN) method are tested. Using the NN residual correction method, the Root Mean Square Error in the Oslo Fjord is reduced by 36% for lead times of one hour and 9% for 24 hours. Therefore, the residual NN method is a promising direction for correcting storm surge forecasts, especially on short timescales. Moreover, it is well adapted to being deployed operationally, as i) the correction is applied on top of the existing model and requires no changes to it, ii) all predictors used for NN inference are already available operationally, iii) prediction by the NNs is very fast, typically a few seconds per station, and iv) the NN correction can be provided to a human expert who may inspect it, compare it with the model output, and see how much correction is brought by the NN, allowing to capitalize on human expertise as a quality validation of the NN output. While no changes to the hydrodynamic model are necessary to calibrate the neural networks, they are specific to a given model and must be recalibrated when the numerical models are updated.

Ana Salagean, Percy Reyes-Paredes

The algebraic degree is an important parameter of Boolean functions used in cryptography. When a function in a large number of variables is not given explicitly in algebraic normal form, it might not be feasible to compute its degree. Instead, one can try to estimate the degree using probabilistic tests. We propose a probabilistic test for deciding whether the algebraic degree of a Boolean function $f$ is below a certain value $k$. The test involves picking an affine space of dimension $k$ and testing whether the values on $f$ on that space sum up to zero. If $deg(f)<k$, then $f$ will always pass the test, otherwise it will sometimes pass and sometimes fail the test, depending on which affine space was chosen. The probability of failing the proposed test is closely related to the number of monomials of degree $k$ in a polynomial $g$, averaged over all the polynomials $g$ which are affine equivalent to $f$. We initiate the study of the probability of failing the proposed ``$deg(f)<k$'' test. We show that in the particular case when the degree of $f$ is actually equal to $k$, the probability will be in the interval $(0.288788, 0.5]$, and therefore a small number of runs of the test is sufficient to give, with very high probability, the correct answer. Exact values of this probability for all the polynomials in 8 variables were computed using the representatives listed by Hou and by Langevin and Leander.

Leonard Hacker, Finn Bartels, Pierre-Etienne Martin

As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes. Each stream is a succession of 3D Convolutional Neural Network (CNN) blocks using attention mechanisms. Each stream processes different 4D inputs. Our method utilizes raw RGB data and pose information computed from MMPose toolbox. The pose information is treated as an image by applying the pose either on a black background or on the original RGB frame it has been computed from. Best performance is obtained by feeding raw RGB data to one stream, Pose + RGB (PRGB) information to the other stream and applying late fusion on the features. The approaches were evaluated on the provided TTStroke-21 data sets. We can report an improvement in stroke classification, reaching 87.3% of accuracy, while the detection does not outperform the baseline but still reaches an IoU of 0.349 and mAP of 0.110.

Gábor Kasza, László P. Csernai, Tamás Csörgő

We present a new family of exact solutions of dissipative fireball hydrodynamics for arbitrary bulk and shear viscosities. The main property of these solutions is a spherically symmetric, Hubble flow field. The motivation of this paper is mostly academic: we apply non-relativistic kinematics for simplicity and clarity. In this limiting case, the theory is particularly clear: the non-relativistic Navier–Stokes equations describe the dissipation in a well-understood manner. From the asymptotic analysis of our new exact solutions of dissipative fireball hydrodynamics, we can draw a surprising conclusion: this new class of exact solutions of non-relativistic dissipative hydrodynamics is asymptotically perfect.

Tim Gorichanaz

Introduction. Hope is a positive attitude oriented toward a possible (yet uncertain), desired outcome. Though hope is a virtue, hopelessness is widespread and seems related not only to current events but also to information about current events. This paper examines how hope can be sparked through information. Method. This study uses the philosophical methods of conceptual analysis and design to advance a theoretical argument. Analysis. First, a conceptualization of hope is offered, drawing on work primarily in virtue ethics. Then, four types of information sources for hope are theorized, building on and synthesizing work from philosophy and psychology. Results. Four categories of information source conducive to hopefulness are identified: information for forming beliefs about the past or future; information for engaging the moral imagination regarding possibilities for the future; information for sparking desire for particular moral outcomes; and information for metacognition, or about how we become informed with respect to hope. Conclusions. Hope is, in many cases, responsive to information. This suggests a moral opportunity for information professionals and scholars to work toward connecting people with information for hope, particularly in difficult times. Avenues for further research, particularly in information behavior and practices, are suggested.

Gustavo Castelo Branco, M. N. Rebelo

In this talk we emphasise the importance of vector-like quarks (VLQs) and their potential to solve some of the open questions of the Standard Model. These are, in some sense minimal extensions of the Standard Model, that can be probed in the next round of experiments. We also make an analogy between vector-like quarks(VLQs) and right-handed neutrinos, emphasising that in both cases some of the flavour dogmas of the SM are violated in a controlled way.

K. Svensberg, B. G. Kalleberg, E. O. Rosvold et al.

Abstract Background An ageing population leads up to increasing multi-morbidity and polypharmacy. This demands a comprehensive and interprofessional approach in meeting patients’ complex needs. This study describes graduate students’ experiences of working practice based in interprofessional teams with complex patients’ care needs in nursing homes. Method Students from advanced geriatric nursing, clinical nutrition, dentistry, medicine and pharmacy at the University of Oslo in Norway were assigned to groups to examine and develop a care plan for a nursing home patient during a course. Focus groups were used, 21 graduate students participating in four groups. Data were collected during spring 2018, were inductively analysed according to a thematic analysis method (Systematic Text Condensation). An analytical framework of co-ordination practices was applied to get an in-depth understanding of the data. Results Three themes were identified: 1) Complex patients as learning opportunities- an eye-opener for future interprofessional collaboration 2) A cobweb of relations, and 3) Structural facilitators for new collective knowledge. Graduate university students experienced interprofessional education (IPE) on complex patients in nursing homes as a comprehensive learning arena. Overall, different co-ordination practices for work organization among the students were identified. Conclusions IPE in nursing homes facilitated the students’ scope from a fragmented approach of the patients towards a relational and collaborative practice that can improve patient care and strengthen understanding of IPE. The study also demonstrated the need for preparatory teamwork training to gain maximum benefit from the experience. Something that can be organized by the education institutions in the form of a stepwise learning module and as an online pre-training course in interprofessional teamwork. Further, focusing on the need for well thought through processes of the activity by the institutions and the timing the practice component in students’ curricula. This could ensure that IPE is experienced more efficient by the students.

Stephen Birch, Terje Alraek, Sascha Gröbe

Mohsen Askar, Raphael Nozal Cañadas, Kristian Svendsen

Background: Network Analysis (NA) is a method that has been used in various disciplines such as Social sciences and Ecology for decades. So far, NA has not been used extensively in studies of medication use. Only a handful of papers have used NA in Drug Prescription Networks (DPN). We provide an introduction to NA terminology alongside a guide to creating and extracting results from the medication networks. Objective: To introduce the readers to NA as a tool to study medication use by demonstrating how to apply different NA measures on 3 generated medication networks. Methods: We used the Norwegian Prescription Database (NorPD) to create a network that describes the co-medication in elderly persons in Norway on January 1, 2013. We used the Norwegian Electronic Prescription Support System (FEST) to create another network of severe drug-drug interactions (DDIs). Lastly, we created a network combining the two networks to show the actual use of drugs with severe DDIs. We used these networks to elucidate how to apply and interpret different network measures in medication networks. Results: Interactive network graphs are made available online, Stata and R syntaxes are provided. Various useful network measures for medication networks were applied such as network topological features, modularity analysis and centrality measures. Edge lists data used to generate the networks are openly available for readers in an open data repository to explore and use. Conclusion: We believe that NA can be a useful tool in medication use studies. We have provided information and hopefully inspiration for other researchers to use NA in their own projects. While network analyses are useful for exploring and discovering structures in medication use studies, it also has limitations. It can be challenging to interpret and it is not suitable for hypothesis testing.

Ian M. Trotter, Torjus F. Bolkesjø, Eirik O. Jåstad et al.

Weather is one of the main drivers of both the power demand and supply, especially in the Nordic region which is characterized by high heating needs and a high share of renewable energy. Furthermore, ambitious decarbonization plans may cause power to replace fossil fuels for heating in the Nordic region, at the same time as large wind power expansions are expected, resulting in even greater exposure to weather risk. In this study, we quantify the increase in weather risk resulting from replacing fossil fuels with power for heating in the Nordic region, at the same time as variable renewable generation expands. First, we calibrate statistical weather-driven power consumption models for each of the countries Norway, Sweden, Denmark, and Finland. Then, we modify the weather sensitivity of the models to simulate different levels of heating electrification, and use 300 simulated weather years to investigate how differing weather conditions impact power consumption at each electrification level. The results show that full replacement of fossil fuels by power for heating in 2040 leads to an increase in annual consumption of 155 TWh (30%) compared to a business-as-usual scenario during an average weather year, but a 178 TWh (34%) increase during a one-in-twenty weather year. However, the increase in the peak consumption is greater: around 50% for a normal weather year, and 70% for a one-in-twenty weather year. Furthermore, wind and solar generation contribute little during the consumption peaks. The increased weather sensitivity caused by heating electrification causes greater total load, but also causes a significant increase in inter-annual, seasonal, and intra-seasonal variations. We conclude that heating electrification must be accompanied by an increase in power system flexibility to ensure a stable and secure power supply.

Joachim Tetteh Siaw, Niloufar Javanmardi, Jimmy Van den Eynden et al.

Summary: High-risk neuroblastomas typically display an undifferentiated or poorly differentiated morphology. It is therefore vital to understand molecular mechanisms that block the differentiation process. We identify an important role for oncogenic ALK-ERK1/2-SP1 signaling in the maintenance of undifferentiated neural crest-derived progenitors through the repression of DLG2, a candidate tumor suppressor gene in neuroblastoma. DLG2 is expressed in the murine “bridge signature” that represents the transcriptional transition state when neural crest cells or Schwann cell precursors differentiate to chromaffin cells of the adrenal gland. We show that the restoration of DLG2 expression spontaneously drives neuroblastoma cell differentiation, highlighting the importance of DLG2 in this process. These findings are supported by genetic analyses of high-risk 11q deletion neuroblastomas, which identified genetic lesions in the DLG2 gene. Our data also suggest that further exploration of other bridge genes may help elucidate the mechanisms underlying the differentiation of NC-derived progenitors and their contribution to neuroblastomas.

Malin Lervåg, Camilla Sørensen, Andreas Robertstad et al.

For many years, the oil and gas industry has utilized superduplex stainless steels due to their high strength and excellent corrosion resistance. Wire arc additive manufacturing (WAAM) was used with superduplex filler wire to create walls with different heat input. Due to the multiple heating and cooling cycles during layer deposition, brittle secondary phases may form such as intermetallic sigma (<i>&#963;</i>) phase. By inspecting deposited walls within wide range of heat inputs (0.40&#8722;0.87 kJ/mm), no intermetallic phases formed due to low inter-pass temperatures used, together with the high Ni content in the applied wire. Lower mechanical properties were observed with high heat inputs due to low ferrite volume fraction, precipitation of Cr nitrides and formation of secondary austenite. The walls showed good toughness values based on both Charpy V-notch and CTOD (crack tip opening displacement) testing.