Hasil untuk "Norwegian literature"

Veronica Kvalen Pilskog, Veronica Kvalen Pilskog

IntroductionHealthcare professionals are expected to remain professionally current and practice in accordance with evidence-based guidelines. However, they frequently encounter barriers such as time constraints, information overload, and limited institutional support. This study explores how physicians and nurses in Norwegian hospitals engage with scientific knowledge in their clinical practice, as well as how various factors at the individual, organizational, and contextual levels influence this engagement. The study draws on the integrated behavioral model, media richness theory, and media synchronicity theory to guide analysis and interpretation.MethodsSemi-structured interviews were conducted with 12 physicians and 10 nurses from emergency, orthopedic, and surgical departments across three Norwegian hospitals. Thematic analysis was then conducted following Braun and Clarke’s six-phase approach, which was informed by the abovementioned theoretical frameworks.ResultsHealthcare professionals relied on a combination of interpersonal communication, digital tools, clinical guidelines, and scientific literature to remain informed. Peer interactions and local protocols were the most frequently used resources. Engagement with scientific knowledge was typically reactive and context-driven, rather than planned. Barriers such as time constraints, cognitive and linguistic challenges, and limited self-efficacy influenced both the selection of information sources and the level of engagement. Clinicians preferred channels that were task-relevant, time-efficient, and easily accessible during clinical workflow.DiscussionThe findings indicate that clinicians adaptively blend different communication channels, often using lean or asynchronous channels for sensemaking tasks traditionally associated with richer formats. While media richness and media synchronicity theories help explain the use of different channels, they may not fully account for how clinicians streamline communication tasks in practice. The integrated behavioral model explains various behavioral patterns, but does not fully capture spontaneous or internally motivated learning. These results emphasize the importance of aligning science communication and organizational support with clinicians’ real-world constraints and preferences.ConclusionAlthough clinicians are motivated to remain current, their engagement with scientific knowledge is influenced by environmental constraints, personal confidence, and cultural norms. Practice increasing scientific knowledge requires not only access to information, but also organizational and social structures that support flexible and responsive learning.

Thomas Compton

Community Unionism has served as a pivotal concept in debates on trade union renewal since the early 2000s, yet its theoretical coherence and political significance remain unresolved. This article investigates why CU has gained such prominence -- not by testing its efficacy, but by mapping how it is constructed, cited, and contested across the scholarly literature. Using two complementary systematic approaches -- a citation network analysis of 114 documents and a thematic review of 18 core CU case studies -- I examine how CU functions as both an empirical descriptor and a normative ideal. The analysis reveals CU's dual genealogy: positioned by British scholars as an indigenous return to historic rank-and-file practices, yet structurally aligned with transnational social movement unionism. Thematic coding shows near-universal emphasis on coalition-building and alliances, but deep ambivalence toward class politics. This tension suggests CU's significance lies less in operationalising a new union model, and more in managing contradictions -- between workplace and community, leadership and rank-and-file, reform and radicalism -- within a shrinking labour movement.

Henri Aïdasso, Mohammed Sayagh, Francis Bordeleau

Continuous Integration (CI) consists of an automated build process involving continuous compilation, testing, and packaging of the software system. While CI comes up with several advantages related to quality and time to delivery, CI also presents several challenges addressed by a large body of research. To better understand the literature so as to help practitioners find solutions for their problems and guide future research, we conduct a systematic review of 97 studies on build optimization published between 2006 and 2024, which we summarized according to their goals, methodologies, used datasets, and leveraged metrics. The identified build optimization studies focus on two main challenges: (1) long build durations, and (2) build failures. To meet the first challenge, existing studies have developed a range of techniques, including predicting build outcome and duration, selective build execution, and build acceleration using caching or repairing performance smells. The causes of build failures have been the subject of several studies, leading to the development of techniques for predicting build script maintenance and automating repair. Recent studies have also focused on predicting flaky build failures caused by environmental issues. The majority of these techniques use machine learning algorithms and leverage build metrics, which we classify into five categories. Additionally, we identify eight publicly available build datasets for build optimization research.

E. C. Overes, F. M. Santoro

Business Process Visualisations (BPVs) have become indispensable tools for organisations seeking to enhance their operational efficiency, decision-making capabilities, and overall performance. The burgeoning interest in process modeling and tool development, coupled with the rise of data visualisation field, underscores the significant role of visual tools in leveraging human cognition. Unlike traditional models, data visualisation approaches graphics from a novel angle, emphasising the potency of visual representations. This review aims to integrate the domains of BPV and data visualisation to assess their combined influence on organisational effectiveness comprehensively. Through a meticulous analysis of existing literature, this study aims to amalgamate insights on BPVs impact from a data visualisation standpoint, advocating for a design philosophy that prioritises user engagement to bolster organisational outcomes. Additionally, our systematic review has unveiled promising avenues for future research, identifying underexplored variables that influence the efficacy of BPVs, thereby charting a path for forthcoming scholarly inquiries.

Baran Shajari, Istvan David

Twin transition is the method of parallel digital and sustainability transitions in a mutually supporting way or, in common terms, "greening of and by IT and data." Twin transition reacts to the growing problem of unsustainable digitalization, particularly in the ecological sense. Ignoring this problem will eventually limit the digital adeptness of society and the problem-solving capacity of humankind. Information systems engineering must find ways to support twin transition journeys through its substantial body of knowledge, methods, and techniques. To this end, we systematically survey the academic and gray literature on twin transition, clarify key concepts, and derive leads for researchers and practitioners to steer their innovation efforts.

Xiang Chen, Jiacheng Xue, Xiaofei Xie et al.

Code translation aims to convert code from one programming language to another automatically. It is motivated by the need for multi-language software development and legacy system migration. In recent years, neural code translation has gained significant attention, driven by rapid advancements in deep learning and large language models. Researchers have proposed various techniques to improve neural code translation quality. However, to the best of our knowledge, no comprehensive systematic literature review has been conducted to summarize the key techniques and challenges in this field. To fill this research gap, we collected 57 primary studies covering the period 2020~2025 on neural code translation. These studies are analyzed from seven key perspectives: task characteristics, data preprocessing, code modeling, model construction, post-processing, evaluation subjects, and evaluation metrics. Our analysis reveals current research trends, identifies unresolved challenges, and shows potential directions for future work. These findings can provide valuable insights for both researchers and practitioners in the field of neural code translation.

Temesgen Kitaw Damenu, İnci Zaim Gökbay, Alexandra Covaci et al.

Educational games have been widely used to teach children about cyber security. This systematic literature review reveals evidence of positive learning outcomes, after analysing 91 such games reported in 68 papers published between 2010 and 2024. However, critical gaps have also been identified regarding the design processes and the methodological rigour, including lack of systematic design, misalignment between proposed and achieved learning outcomes, rare use of control groups, limited discussions on ethical considerations, and underutilisation of emerging technologies. We recommend multiple future research directions, e.g., a hybrid approach to game design and evaluation that combines bottom-up and top-down approaches.

Thanh Nguyen, Chaima Boufaied, Ronnie de Souza Santos

Today, with the growing obsession with applying Artificial Intelligence (AI), particularly Machine Learning (ML), to software across various contexts, much of the focus has been on the effectiveness of AI models, often measured through common metrics such as F1- score, while fairness receives relatively little attention. This paper presents a review of existing gray literature, examining fairness requirements in AI context, with a focus on how they are defined across various application domains, managed throughout the Software Development Life Cycle (SDLC), and the causes, as well as the corresponding consequences of their violation by AI models. Our gray literature investigation shows various definitions of fairness requirements in AI systems, commonly emphasizing non-discrimination and equal treatment across different demographic and social attributes. Fairness requirement management practices vary across the SDLC, particularly in model training and bias mitigation, fairness monitoring and evaluation, and data handling practices. Fairness requirement violations are frequently linked, but not limited, to data representation bias, algorithmic and model design bias, human judgment, and evaluation and transparency gaps. The corresponding consequences include harm in a broad sense, encompassing specific professional and societal impacts as key examples, stereotype reinforcement, data and privacy risks, and loss of trust and legitimacy in AI-supported decisions. These findings emphasize the need for consistent frameworks and practices to integrate fairness into AI software, paying as much attention to fairness as to effectiveness.

Heidi Gilstad

Background Health literacy refers to how people acquire, understand, communicate, and act on health information to improve their health.Aims The study aims to examine how health literacy manifests communicatively in patient-nurse consultations.Methods The study design is qualitative, with observational data from 10 video recordings of nurse-patient follow-up consultations in a rheumatology clinic. With a theme-oriented discourse analytic approach, we study health literacy in the interaction between the nurse and patient. The study combines analytic themes from professional discourse studies (frames, facework, rhetoric devices) with focal themes relevant to professional practice (knowledge types). It examines three representative examples to analyze how health literacy manifests in interaction.Results The analyses show how nurses and patients negotiate patients’ knowledge in relation to medical professional and nonprofessional frames during the consultation. Different knowledge types manifest communicatively, for example embodied, monitoring, and navigating knowledge. The nurses allow patients′ narratives; through facework, they negotiate, reformulate and expand the patients’ knowledge.Discussion Patients health literacy is expressed in their interactions with the nurses in the clinic and includes aspects regarding the patient self, institutional and professional aspects of the system, and social and cultural aspects of the community. The insights provided by this study may increase nurses’ awareness of how to nuance and activate patients’ health literacy throughout the consultation. The study enriches ethnographically based communication research on health literacy by integrating a discourse analytic approach with nuanced types of knowledge and illustrative examples from authentic cases.

Yutong Xie, Yijun Pan, Hua Xu et al.

Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI methods in driving broad scientific discovery. Existing efforts in identifying and bridging this gap have often relied on qualitative examination of small samples of literature, offering a limited perspective on the broader AI4Science landscape. In this work, we present a large-scale analysis of the AI4Science literature, starting by using large language models to identify scientific problems and AI methods in publications from top science and AI venues. Leveraging this new dataset, we quantitatively highlight key disparities between AI methods and scientific problems, revealing substantial opportunities for deeper AI integration across scientific disciplines. Furthermore, we explore the potential and challenges of facilitating collaboration between AI and scientific communities through the lens of link prediction. Our findings and tools aim to promote more impactful interdisciplinary collaborations and accelerate scientific discovery through deeper and broader AI integration. Our code and dataset are available at: https://github.com/charles-pyj/Bridging-AI-and-Science.

Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai et al.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Stefan Langer, Fabian Neuhaus, Andreas Nürnberger

Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.

Shahbaz Syed, Khalid Al-Khatib, Martin Potthast

This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization. It organizes 514~papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search. Each paper was manually annotated to capture aspects such as evaluation metrics, quality dimensions, learning paradigms, challenges addressed, datasets, and document domains. In addition, a succinct indicative summary is provided for each paper, consisting of automatically extracted contextual factors, issues, and proposed solutions. The tool is available online at https://www.tldr-progress.de, a demo video at https://youtu.be/uCVRGFvXUj8

Ting He, Kory Kreimeyer, Mimi Najjar et al.

The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings described in biomedical literature and several other sources. We evaluated the potential contributions of specific natural language processing solutions to support knowledge discovery from biomedical literature. Two models from the Bidirectional Encoder Representations from Transformers (BERT) family, two Large Language Models, and PubTator 3.0 were tested for their ability to support the named entity recognition (NER) and the relation extraction (RE) tasks. PubTator 3.0 and the BioBERT model performed best in the NER task (best F1-score equal to 0.93 and 0.89, respectively), while BioBERT outperformed all other solutions in the RE task (best F1-score 0.79) and a specific use case it was applied to by recognizing nearly all entity mentions and most of the relations.

Muhammad Raees, Inge Meijerink, Ioanna Lykourentzou et al.

AI systems are increasingly being adopted across various domains and application areas. With this surge, there is a growing research focus and societal concern for actively involving humans in developing, operating, and adopting these systems. Despite this concern, most existing literature on AI and Human-Computer Interaction (HCI) primarily focuses on explaining how AI systems operate and, at times, allowing users to contest AI decisions. Existing studies often overlook more impactful forms of user interaction with AI systems, such as giving users agency beyond contestability and enabling them to adapt and even co-design the AI's internal mechanics. In this survey, we aim to bridge this gap by reviewing the state-of-the-art in Human-Centered AI literature, the domain where AI and HCI studies converge, extending past Explainable and Contestable AI, delving into the Interactive AI and beyond. Our analysis contributes to shaping the trajectory of future Interactive AI design and advocates for a more user-centric approach that provides users with greater agency, fostering not only their understanding of AI's workings but also their active engagement in its development and evolution.

Pedro Miguel Moás, Carla Teixeira Lopes

Wikipedia is the world's largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

Huixue Zhou, Robin Austin, Sheng-Chieh Lu et al.

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.

Varish Mulwad, Tim Finin, Vijay S. Kumar et al.

Entity linking is an important step towards constructing knowledge graphs that facilitate advanced question answering over scientific documents, including the retrieval of relevant information included in tables within these documents. This paper introduces a general-purpose system for linking entities to items in the Wikidata knowledge base. It describes how we adapt this system for linking domain-specific entities, especially for those entities embedded within tables drawn from COVID-19-related scientific literature. We describe the setup of an efficient offline instance of the system that enables our entity-linking approach to be more feasible in practice. As part of a broader approach to infer the semantic meaning of scientific tables, we leverage the structural and semantic characteristics of the tables to improve overall entity linking performance.

Georgiana BOZÎNTAN

Nils-Øivind Haagensen is a Norwegian poet, writer, journalist and the head of Flamme Publishing House. He debuted as a poet in 1998 with the volume Hender og hukommelse (Hands and Memories) and as a novelist in 2001 with Det radioaktige (The Radioactive). In 2013 he was nominated for Nordisk råds litteraturpris (Nordic Council Literature Prize) for the volume of poetry God morgen og god natt (Good Morning and Good Night, 2012). Among his latest publications are the novels Dette norske livet (This Norwegian Life, 2019) and Sangria i parken (Sangria in the Park, 2021). The novel Er hun din? (Is She Yours?) was published in 2016 at Oktober Publishing House in Norway and in 2023 it was translated into Romanian by Raluca-Daniela Duinea, being Haagensen’s first translation published in Romania at Casa Cărții de Știință Publishing House, the Nordica Collection, in Cluj-Napoca, with financial support from NORLA (Norwegian Literature Abroad).

Angeliki-Astero Papathanasiou, R. Cole, Philip Murray

Abstract The implementation of blockchain technology (BCT) is gaining traction in supply chain networks, revolutionising the operation of contemporary supply chains and reshaping the potential of business relationships. Empirical studies on blockchain adoption are scant because implementation across networks is in fairly early phase of development, yet evidence from empirical studies is highly desirable. This is one of the first studies of blockchain adoption in the Greek shipping industry, which has not so far been examined by the literature, in direct comparison to early adopters in other European countries such as Norway. The research examined eight Greek shipping companies using workshops with experienced supply chain personnel. Qualitative analysis identified the current position of these organisations in terms of blockchain adoption, by considering possible benefits and inhibitors to implementation. Despite benefits of automated processes and reduced paperwork as a result of smart contracts, findings show a reluctance to adopt BCT. That is, enterprise resource planning (ERP) transformations have left organisations fatigued and disinclined towards further systems development and resistant to subsequent change. Also, the exposure of shared information in the shipping nexus is considered to cause a threat to competitive survival.