Hasil untuk "Norwegian literature"

Heming H. Gujord

In his 17 novels, Kjartan Fløgstad (born 1944) has analysed the traces of WWII and possible continuations of right-wing ideology into post-war politics and ideology. In my article, I focus on four novels: <i>Dalen Portland</i>, <i>U3</i>, <i>Grense Jakobselv</i>, and <i>Due og drone</i> (<i>Dove and Drone</i>). <i>Dalen Portland</i> and <i>U3</i> were published in the context of the Cold War, whereas <i>Grense Jakobselv</i> and <i>Due og drone</i> were published in a context in which history was claimed to have reached its end after the collapse of the Soviet Union. Fløgstad has opposed the end-of-history thesis since it was introduced in the influential study by Francis Fukuyama in 1992. From Fløgstad’s perspective, history has reached a dead end, as democratic ideals are being challenged and economic disparities are widening—even within the welfare states of Northern Europe. In all the novels being discussed, Fløgstad has consistently focused on factual and possible interlinks between right-wing figures of thought and stakeholders of political and economic power. Thus, the only consistent superpower, the United States, has also been an object of Fløgstad’s interest. The importance of the United States is even indicated in the well-chosen title for the English translation of <i>Dalen Portland</i>: <i>Dollar Road</i>. The interpretation of Fløgstad’s novels is simultaneously an interpretation of history. Given the threats to democratic ideals that have emerged in the 2020s, Fløgstad’s analysis has demonstrated notable foresight.

Aritra Roy, Enrico Grisan, John Buckeridge et al.

Since the advent of various pre-trained large language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional machine learning or natural language processing techniques. Despite these advances, accessible automated tools that allow users to construct, validate, and visualise datasets from scientific literature extraction remain scarce. We therefore developed ComProScanner, an autonomous multi-agent platform that facilitates the extraction, validation, classification, and visualisation of machine-readable chemical compositions and properties, integrated with synthesis data from journal articles for comprehensive database creation. We evaluated our framework using 100 journal articles against 10 different LLMs, including both open-source and proprietary models, to extract highly complex compositions associated with ceramic piezoelectric materials and corresponding piezoelectric strain coefficients (d33), motivated by the lack of a large dataset for such materials. DeepSeek-V3-0324 outperformed all models with a significant overall accuracy of 0.82. This framework provides a simple, user-friendly, readily-usable package for extracting highly complex experimental data buried in the literature to build machine learning or deep learning datasets.

Bianca Leech, Ridewaan Hanslo

The rapid evolution of IT projects has driven the transformation of project management methodologies, from traditional waterfall approaches to agile frameworks and, more recently, hybrid models. This systematic literature review investigates the evolution of agile methodologies into hybrid frameworks, analysing their implementation challenges and success factors. We identify key trends through PRISMA-guided analysis of peer-reviewed studies from the last 8 years. Hybrid methodologies emerge from agile limitations in large-scale and regulated environments, combining iterative flexibility with structured governance. Agile has several implementation challenges, leading to hybrid methods, and the success hinges on leadership support, tailored process integration, and continuous improvement mechanisms. The study explores the need for contextual adaptation over rigid frameworks, offering practical insights for organisations navigating hybrid transitions.

Logan A. Pearce, Jared R. Males, Mary Anne Limbach

The next generation of ground- and space-based observatories will enable direct imaging and characterization of cold, mature planets through thermal emission and, for the first time, reflected light detection. Known RV and astrometrically detected planets provide a known population for detection and characterization observations. However, many of the most promising targets lack orbital parameters of sufficient precision to confidently predict their location on relative to the star for a direct imaging campaign. We have developed \texttt{projecc}, an open source Python package designed to generate sky-plane planet location posteriors from literature orbit solutions. This tool aims to facilitate community preparation for direct imaging observations of known planets. In this work we describe \texttt{projecc} and use it to examine two case study systems relevant to reflected light imaging with ELTs: GJ~876~b, which we find has a well-constrained prediction, and Proxima Centauri b, whose location remains highly uncertain.%, as well as one potential target for \textsl{Roman} CGI, HD~219134~h, which we estimate has a 40\% probability of being in a detectable sky location at any given time. We provide a web app for exploring reflected light planet targets and their orbit solutions, including predictions from literature for 17 additional planets, located at https://reflected-light-planets.streamlit.app/. We also discuss future upgrades to \texttt{projecc}.

Rui Zhou, Haohui Ma, Tianle Xin et al.

The rapid expansion of enzyme kinetics literature has outpaced the curation capabilities of major biochemical databases, creating a substantial barrier to AI-driven modeling and knowledge discovery. We present zERExtractor, an automated and extensible platform for comprehensive extraction of enzyme-catalyzed reaction and activity data from scientific literature. zERExtractor features a unified, modular architecture that supports plug-and-play integration of state-of-the-art models, including large language models (LLMs), as interchangeable components, enabling continuous system evolution alongside advances in AI. Our pipeline combines domain-adapted deep learning, advanced OCR, semantic entity recognition, and prompt-driven LLM modules, together with human expert corrections, to extract kinetic parameters (e.g., kcat, Km), enzyme sequences, substrate SMILES, experimental conditions, and molecular diagrams from heterogeneous document formats. Through active learning strategies integrating AI-assisted annotation, expert validation, and iterative refinement, the system adapts rapidly to new data sources. We also release a large benchmark dataset comprising over 1,000 annotated tables and 5,000 biological fields from 270 P450-related enzymology publications. Benchmarking demonstrates that zERExtractor consistently outperforms existing baselines in table recognition (Acc 89.9%), molecular image interpretation (up to 99.1%), and relation extraction (accuracy 94.2%). zERExtractor bridges the longstanding data gap in enzyme kinetics with a flexible, plugin-ready framework and high-fidelity extraction, laying the groundwork for future AI-powered enzyme modeling and biochemical knowledge discovery.

Adi Wijaya, Said Hasibuan, Wiga Maulana Baihaqi et al.

Application of electroencephalography (EEG) in educational research has grown substantially, yet a comprehensive integration of methodological frameworks, educational constructs, computational methods, and research gaps remains limited. This study applies a Bibliometric-enhanced Systematic Literature Review (BenSLR) to provide a systematic overview of EEG in education. Literature was extracted from Scopus, screened, and analyzed, with keyword co-occurrence evaluated using VOSviewer and emerging trends visualized through an Enhanced Strategic Diagram via BiblioPlot. Key findings include engagement, attention, and learning style as prominent constructs, with machine learning and deep learning frequently employed for modeling complex cognitive states. EEG signal processing, feature extraction, and assessment of cognitive and affective states were recurrent across studies. Innovative interventions such as virtual reality and neurofeedback demonstrate EEG's role in supporting adaptive and individualized learning experiences. Challenges remain in linking neural markers with observable learning behaviors, extending measurements beyond attention and working memory, and enhancing predictive model generalizability. The study demonstrates BenSLR's potential to integrate qualitative and quantitative perspectives and offers a transferable approach for other research areas to develop methodologies and evidence-based educational interventions.

Kaya Cetin, Andrea Melberg

Background: Even in wealthy Norway, there is a mismatch between medical need and the resources at hand given an aging population and technological advancements: priorities need to be made. Priority setting decisions occur at many levels of the healthcare system, and many resource allocation decisions are made by hospital leaders. The meso-level refers to healthcare service management at referral institutions, distinct from macro-level policymaking and micro-level clinical decisions. Aim: The process of setting priorities in the hospital setting remains unclear and insufficiently investigated. This review aims at making an overview of available research on priority setting at the meso-level. Method: We conducted a scoping review to gather data from research on priority setting and decision making in Norwegian hospitals. Our methodology followed PRISMA-scoping review principles, including a systematic literature search of the databases PubMed, Idunn, WoS, and CINAHL, and a manual search of reference lists. We included 11 empirical studies, covering various research objectives and domains, such as nursing ethics and costing analysis tool evaluation. Results: Using the Walt and Gilson policy analysis framework, we identified key elements of priority setting: content, context, process, and actors. Our analysis revealed a lack of empirical findings on the process domain. Conclusion: Scientific knowledge about priority setting remains centred around the content of policies, particularly criteria and guidelines. The focus on content overshadows procedural aspects within the policy analysis triangle. Understanding the processes of priority setting is crucial for the legitimacy of the public sector in a democratic welfare state.

Viktoriia Koilo

Type of the article: Research Article AbstractThis study investigates whether public investment in green maritime infrastructure and transportation projects contributes to CO₂ emission reductions in Norwegian port municipalities. Using panel data for 28 coastal municipalities from 2016 to 2023, the analysis assesses the temporal effects of maritime-supported projects, technology development (TransMar) and infrastructure (InfraMar), on local maritime emissions. A two-stage empirical strategy combines lagged panel regressions (OLS, FE, and RE) with unsupervised clustering to uncover structural heterogeneity in port activity and investment profiles. The results reveal that transport-related investments exhibit statistically significant emission-reducing effects with lags of three to five years, supporting the long-term decarbonization potential of targeted funding. In contrast, green infrastructure presence correlates positively with emissions, likely reflecting higher port activity and policy targeting in high-emission areas. Clustering analysis confirms that municipalities differ substantially in activity levels, investment patterns, and emission profiles, reinforcing the case for differentiated policy strategies. The findings contribute to environmental economics and maritime policy by offering new micro-level evidence on the effectiveness and temporal dynamics of green investments. This paper extends previous literature by integrating spatial clustering with dynamic investment modeling, providing novel insights into how policy timing and local industrial structure shape emission outcomes. The results have direct implications for designing adaptive, region-specific maritime decarbonization programs and guiding future EU-aligned infrastructure strategies.

Marco De Vincenzi, Mert D. Pesé, Chiara Bodei et al.

The growing reliance on software in road vehicles has led to the emergence of Software-Defined Vehicles (SDV). This work analyzes SDV security and privacy through a systematic literature review complemented by an industry questionnaire across the automotive supply chain. The analysis is structured as four research questions and results in a security framework serving as a roadmap for SDV protection. The findings emphasize addressing mixed-criticality architectural challenges, deploying layered security mechanisms, and integrating privacy-preserving techniques. The results highlight the need to harmonize in-vehicle and cloud-based defenses to strengthen cybersecurity and V2X resilience in Intelligent Transportation Systems (ITS).

Cristiano Salvagnin

This study conducts a bibliometric review of scientific literature on the European Union Emissions Trading System (EU ETS) from 2004 to 2024, using research articles from the Scopus database. Using the Bibliometrix R package, we analyze publication trends, key themes, influential authors, and prominent journals related to the EU ETS. Our results indicate a notable increase in research activity over the past two decades, particularly during significant policy changes and economic events affecting carbon markets. Key research focuses include carbon pricing, market volatility, and economic impacts, highlighting a shift toward financial analysis and policy implications. Thematic mapping shows cap-and-trade systems, and carbon leakage as central topics linking various research areas. Additionally, we observe key areas where further research could be beneficial, such as expanding non-parametric methodologies, deepening the exploration of macroeconomic factors, and enhancing the examination of financial market connections. Moreover, we highlight recent and innovative papers that contribute new insights, showcasing emerging trends and cutting-edge approaches within the field. This review provides insights for researchers and policymakers, highlighting the evolving landscape of EU ETS research and its relevance to global climate strategies.

Simon Schneider, Ananya Saha, Emanuele Mezzi et al.

AI-based systems leverage recent advances in the field of AI/ML by combining traditional software systems with AI components. Applications are increasingly being developed in this way. Software engineers can usually rely on a plethora of supporting information on how to use and implement any given technology. For AI-based systems, however, such information is scarce. Specifically, guidance on how to securely design the architecture is not available to the extent as for other systems. We present 16 architectural security guidelines for the design of AI-based systems that were curated via a multi-vocal literature review. The guidelines could support practitioners with actionable advice on the secure development of AI-based systems. Further, we mapped the guidelines to typical components of AI-based systems and observed a high coverage where 6 out of 8 generic components have at least one guideline associated to them.

Flavio Corradini, Flavio Gerosa, Marco Gori et al.

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git providing additional interactive tools to further explore the presented findings.

Monica Erevik Baer-Olsen, Tove Dragesund, Randi Sviland

Background: Many individuals arriving in Norway has experienced torture, but there is limited knowledge about physiotherapy for torture survivors. The purpose of this study is to examine how Norwegian Psychomotor Physiotherapy (NPMF) can be adapted for torture survivors, as well as exploring the opportunities and challenges in approaching this patient group. Method: The study is based on qualitative data from in-depth interviews with four psychomotor physiotherapists who have experience with treatment for torture survivors. This data forms the basis for a cross-cutting analysis. Trauma theory, culturally sensitivity trauma awareness and relevant literature and studies from NPMF constitute the study's theoretical frame of reference. Findings: The consequences of torture uniquely challenge the significance of a therapeutic alliance. Establishing a safe and trusting alliance requires time and openness. It is essential to be attentive and respectful of cultural differences and backgrounds, both in the patient and the therapists. Simultaneously, the treatment must be continuously adapted to the patient's individual needs to prevent re-traumatization. Conclusion: NPMF can be a significant approach in the rehabilitation of torture survivors. Through a trusting therapeutic alliance, a culture- and trauma- sensitive approach, along with individual adjustments, NPMF can contribute to a better connection with the body and increased sense of security. However, this requires expertise and patience.

Nikolaos Misirlis

This review examines the scientific articles of the last decade, approaching the subject through the methodology of the scoping literature review. Starting with the Boolean search global citizens AND education AND (international business OR international business school) in the ScienceDirect, Emerald, and Scopus databases, the review resulted in only scientific journal articles, strictly targeted at tertiary education ONLY of international business schools and ONLY in those articles that study global citizenship. For reasons of up-to-date knowledge, the present literature was content with the final decade. A total of 13 articles are recorded as a result of the aforementioned Boolean search from a total of 216 articles identified in the first phase of the search. The results will help the researchers to acquire the required knowledge base for their research, the academics to incorporate new methods in their teaching and the approach of their students, and the policymakers to adapt the schools curricula according to the data from the articles present in the literature review.

Xiaofeng Wang, Henry Edison, Dron Khanna et al.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Priyanka Kaswan, Purbesh Mitra, Arunabh Srivastava et al.

Gossiping is a communication mechanism, used for fast information dissemination in a network, where each node of the network randomly shares its information with the neighboring nodes. To characterize the notion of fastness in the context of gossip networks, age of information (AoI) is used as a timeliness metric. In this article, we summarize the recent works related to timely gossiping in a network. We start with the introduction of randomized gossip algorithms as an epidemic algorithm for database maintenance, and how the gossiping literature was later developed in the context of rumor spreading, message passing and distributed mean estimation. Then, we motivate the need for timely gossiping in applications such as source tracking and decentralized learning. We evaluate timeliness scaling of gossiping in various network topologies, such as, fully connected, ring, grid, generalized ring, hierarchical, and sparse asymmetric networks. We discuss age-aware gossiping and the higher order moments of the age process. We also consider different variations of gossiping in networks, such as, file slicing and network coding, reliable and unreliable sources, information mutation, different adversarial actions in gossiping, and energy harvesting sensors. Finally, we conclude this article with a few open problems and future directions in timely gossiping.

Siyuan Wu, Ruijuan Xiao, Hong Li et al.

A potential superionic material LiY(SO$_4$)$_2$ has been excavated from the published literatures because its synthesis method and experiment data implied it exists the superionic state. We use \textit{ab initio} calculation to analyzing the differences between solid state and superionic state. We found the diffusion of Li$^+$ from the lattice site to the interstitial site will change the nearest neighbor numbers of O atom from 4 to 8. In order to reduce energy, the reorientation of SO$_4^{2-}$ must exist accompany with the diffusion of Li$^+$ so the nearest neighbor number of O will keep about 5 in the superionic state. Our work not only presents an example for discovering materials from literatures based on prior knowledge but also reveals the micromechanism of cation-anion coupled dynamics for superionic state.

Workneh Y. Ayele, Gustaf Juell-Skielse

Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally server as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored.

Benedikt Jager

Artikkelen undersøker Ingvar Ambjørnsens novelle «Skogens hjerte» fra novellesamlingen Natt til mørk morgen (1997) og fokuserer på fremstillingen av ruserfaringer som står sentralt i teksten. Utgangspunktet for lesningen er Peter Sloterdijks beskrivelse av den historiske utviklingen av ruserfaringer fra antikken til det moderne. Hans forståelse finner man også i Ambjørnsens korttekst, men denne omfatter dessuten en bestemt bruk av intertekstualitet. Herved fokuseres det både på litterære (f.eks. Vesaas) og idéhistoriske referanser (Huxley). «Skogens hjerte» ble i tillegg det intertekstuelle forelegget for deler av Ambjørnsens roman Natten drømmer om dagen (2012). På en måte ‘sampler’ forfatteren sin egen novelle, som får en mer pessimistisk omfortolking.

Svein Gunnar Sjøtun, Arnt Fløysand, Heidi Wiig et al.

This article analyzes the role of agency in reducing environmental risk in the Norwegian salmon farming industry. The theoretical starting point is recent literature on change agency which focuses on the different ways in which actors purposely act to renew existing and create new regional industry growth paths, and reproductive agency which focuses on how actors, explicitly and implicitly, maintain existing structures to uphold status quo. Departing from a current risk society ambiguity in the industry and an explorative multi-scalar study of industrial innovation processes, we analysis how change agency combined with reproductive agency play out. The analysis shows that change agency affecting transformative agency capacity reducing environmental risk is connected to institutional entrepreneurship in terms of a Development Licenses Program on the national level and to Schumpeterian innovative entrepreneurship in terms of Development Licenses Projects on firm level. Moreover, the study shows how reproductive agency also affects the capacity to cope with environmental risks in terms of risk reducing place-based leadership illustrated by cooperation and bottom-up, self-organized area cooperation on the regional level, and in terms of risk creation illustrated by a global growth logic across geographical levels. On this ground, it is argued that the theoretical contribution of the study is that the transformative capacity to reduce environmental risks of an industry rests on multi-scalar change- and reproductive agency and how these are combined.