Hasil untuk "Norwegian literature"

Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada et al.

Recent research on ray tracing cores has explored repurposing these cores to solve non-graphical problems by reformulating them as geometric queries, leveraging the inherent parallelism of ray tracing. Although successful in specific cases, these applications lack a clear pattern, and the conditions under which RT cores can provide computational benefits are still not clearly understood. The objective of this literature review is to examine diverse applications of ray tracing cores in general-purpose computation, identifying common features, performance gains, and limitations. By categorizing these efforts, the review aims to provide guidance on the types of problems that can effectively exploit ray tracing hardware beyond traditional rendering tasks. This is achieved with a blibliometric review based on 59 research articles indexed in Scopus, and a systematic literature review on 35 of them which propose new RT solutions and compare them with state-of-the-art methods to solve 32 distinct problems, in some works achieving up to $200\times$ speedup. Most of the problems analyzed in this work have applications in physics simulations and in solving some geometric queries, but problems with potential applications in databases and AI can also be found. Analyzing the characteristics of the problems, it was found that nearest neighbor search, including its variants, benefit the most from ray tracing cores as well as problems that rely on heuristic to diminish the necessary work. This is aligned with the biggest strength of RT cores; discarding tree branches when traversing a tree to avoid unnecessary work. Also, it was found that many short-length rays should be preferred over a few large rays. The results found in this work can serve as a guide for knowing beforehand which applications are better potential candidates to benefit from RT Core computation.

Xiaowen Zhang, Zhenyu Bi, Patrick Lachance et al.

As cosmological simulations and their associated software become increasingly complex, physicists face the challenge of searching through vast amounts of literature and user manuals to extract simulation parameters from dense academic papers, each using different models and formats. Translating these parameters into executable scripts remains a time-consuming and error-prone process. To improve efficiency in physics research and accelerate the cosmological simulation process, we introduce SimAgents, a multi-agent system designed to automate both parameter configuration from the literature and preliminary analysis for cosmology research. SimAgents is powered by specialized LLM agents capable of physics reasoning, simulation software validation, and tool execution. These agents collaborate through structured communication, ensuring that extracted parameters are physically meaningful, internally consistent, and software-compliant. We also construct a cosmological parameter extraction evaluation dataset by collecting over 40 simulations in published papers from Arxiv and leading journals that cover diverse simulation types. Experiments on the dataset demonstrate a strong performance of SimAgents, highlighting its effectiveness and potential to accelerate scientific research for physicists. Our demonstration video is available at: https://youtu.be/w1zLpm_CaWA. The complete system and dataset are publicly available at https://github.com/xwzhang98/SimAgents.

Muhammad Laiq

In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, particularly focusing on use case and sequence diagrams. Through a term-based search, we filtered and identified 14 relevant primary studies. Our analysis of the selected primary studies reveals that LLMs have demonstrated promising results in automatically generating use case and sequence diagrams. In addition, we found that most of the current literature lacks expert-based evaluations and has mainly used GPT-based models. Therefore, future work should evaluate a broader range of LLMs for behavioral modeling and involve domain experts to evaluate the output of LLMs.

Ramiro Pesina

Mentoring software is a pivotal innovation in addressing critical challenges in teacher development within educational institutions. This study explores the transformative potential of digital mentoring platforms, evaluating their impact on enhancing traditional mentoring practices through scalable, data-driven, and accessible frameworks. The research synthesizes findings from existing literature to assess the effectiveness of key features, including structured goal setting, progress monitoring, and advanced analytics, in improving teacher satisfaction, retention, and professional growth. Using an integrative literature review approach, this study identifies both the advantages and barriers to implementing mentoring software in education. Financial constraints, limited institutional support, and data privacy concerns remain significant challenges, necessitating strategic interventions. Drawing insights from successful applications in healthcare and corporate sectors, the review highlights adaptive strategies such as leveraging open-source tools, cross-sector collaborations, and integrating mentoring software with existing professional development frameworks. The research emphasizes the necessity of integrating digital mentoring tools with institutional objectives to create enduring support systems for teacher development. Mentoring software not only enhances traditional mentorship but also facilitates broader professional networks that contribute to collective knowledge sharing.

Sara Beschi, Davide Falessi, Silvia Golia et al.

With the advent of the data era, and of new, more intelligent interfaces for supporting decision making, there is a growing need to define, model and assess human ability and data visualizations usability for a better encoding and decoding of data patterns. Data Visualization Literacy (DVL) is the ability of encoding and decoding data into and from a visual language. Although this ability and its measurement are crucial for advancing human knowledge and decision capacity, they have seldom been investigated, let alone systematically. To address this gap, this paper presents a systematic literature review comprising 43 reports on DVL, analyzed using the PRISMA methodology. Our results include the identification of the purposes of DVL, its satellite aspects, the models proposed, and the assessments designed to evaluate the degree of DVL of people. Eventually, we devise many research directions including, among the most challenging, the definition of a (standard) unifying construct of DVL.

Benja Stig Fagerland, Ole Boe , Søren Obed Madsen

The Norwegian Police University College (NPUC) is introducing sustainability leadership as a part of its police leadership studies. At present, the NPUC has no curriculum that contains any literature on sustainable leadership in the police. Thus, our two research questions were: 1. What has been written in the research literature about sustainable leadership in the police? 2. How is the concept of sustainable leadership in the police used in learning and education of police students? We decided to conduct a systematic literature review using the search terms sustainable leadership and police. We searched the databases Academic Search Premier, Criminal Justice Abstract with Full Text, PsycInfo and Scopus as they seemed to be the most relevant databases in order to answer our research questions. The results from this search were only eight unique studies that dealt with our topic of interest. The identification of studies via databases and registers was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. We draw the conclusion that there is an almost complete lack of research and curriculum literature on sustainable leadership in the police that probably will lead to unforeseen challenges for education and learning for NPUC´s students.

Sindre Bangstad

Decolonization has been something of a buzzword in numerous academic disciplines as well as in activism  in recent years. In its original conception, it refers to the formal end to colonial rule and to political sovereignty, but the concept cannot be reduced to this. There is a crucial distinction to be made between (a) decolonization and (b) the decolonial/decoloniality. The case I will use here is the 2015-16 #RhodesMustFall movement in South Africa. I argue for the importance of studying calls for decolonization, and the question of who mobilizes around calls for decolonization and their attended concepts, and for what purposes they do so. Following Mbembe (2016, 2019, 2020), I also argue for a productive engagement with, rather than wholesale condemnation of, calls to “decolonize” and of decolonizing and/or decolonial literature.  I identify this as a “middle ground” of decolonization.

Tor-Olav Nævestad, Ingeborg Storesund Hesjevoll, Rune Elvik et al.

In this study, we provide a scoping review of the research literature on Vision Zero, Safe System and Sustainable Safety. Using a simplified PRISMA approach, we identify 129 studies, describing what year and where the studies are from, which topics the studies are about, and how the terms are used. Using a thematic analysis of the abstracts of the 129 studies, we identify seven main study topics in the studies. The most prevalent study topics are: 1) Case study/Implementation study, 2) Study on principles, 3) Study on practical/strategic use, 4) Study on "readiness", or factors that inhibit or promote implementation, 5) Vulnerable road users, inequality and social justice, 6) Results of measures and potential, and 7) Future challenges and solutions. We describe knowledge status, knowledge gaps and questions for future research within each study topic. The studies find that entities that have formally implemented the Safe System have relatively low levels of implementation, due to implementation barriers and operationalization challenges. The studies find that it is not necessarily clearly defined what Vision Zero/Safe System is in practice, and that the concept must be interpreted and translated by those who will implement it. Thus, current road safety policies do not fully realise the potential improvements that Safe System can provide, because the principles are not followed to a sufficient extent. A Norwegian study estimate that the number of road fatalities in Norway can be reduced by 50%–70% by following the Safe System principles fully and systematically. Thus, a major challenge, also in mature Safe System contexts, is to facilitate actual Safe System implementation, by mapping barriers/facilitators and Safe System readiness, and defining actions to realise the full potential of Safe System implementation.

Seyed Mohammad Ali Jafari

The escalating volume of academic literature presents a formidable challenge in staying updated with the newest research developments. Addressing this, this study introduces a pioneering AI-based tool, configured specifically to streamline the efficiency of the article selection phase in Systematic Literature Reviews (SLRs). Utilizing the robust capabilities of OpenAI's GPT-4 Assistant API, the tool successfully homogenizes the article selection process across a broad array of academic disciplines. Implemented through a tripartite approach consisting of data preparation, AI-mediated article assessment, and structured result presentation, this tool significantly accelerates the time-consuming task of literature reviews. Importantly, this tool could be highly beneficial in fields such as management and economics, where the SLR process involves substantial human judgment. The adoption of a standard GPT model can substantially reduce potential biases and enhance the speed and precision of the SLR selection phase. This not only amplifies researcher productivity and accuracy but also denotes a considerable stride forward in the way academic research is conducted amidst the surging body of scholarly publications.

Xin Zhou, Sicong Cao, Xiaobing Sun et al.

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading SE, AI, and Security conferences and journals, encompassing 43 papers published across 25 distinct venues, along with 15 high-quality preprint papers, bringing the total to 58 papers. By answering three key research questions, we aim to (1) summarize the LLMs employed in the relevant literature, (2) categorize various LLM adaptation techniques in vulnerability detection, and (3) classify various LLM adaptation techniques in vulnerability repair. Based on our findings, we have identified a series of limitations of existing studies. Additionally, we have outlined a roadmap highlighting potential opportunities that we believe are pertinent and crucial for future research endeavors.

Suchith Reddy Vemula, Marcia Moraes

Learning Analytics Dashboard for Advisors is designed to provide data-driven insights and visualizations to support advisors in their decision-making regarding student academic progress, engagement, targeted support, and overall success. This study explores the current state of the art in learning analytics dashboards, focusing on specific requirements for advisors. By examining existing literature and case studies, this research investigates the key features and functionalities essential for an effective learning analytics dashboard tailored to advisor needs. This study also aims to provide a comprehensive understanding of the landscape of learning analytics dashboards for advisors, offering insights into the advancements, opportunities, and challenges in their development by synthesizing the current trends from a total of 21 research papers used for analysis. The findings will contribute to the design and implementation of new features in learning analytics dashboards that empower advisors to provide proactive and individualized support, ultimately fostering student retention and academic success.

Veronica Santos

A data model specifies how real-world entities and their relationships are represented and operated. In the NoSQL world data modeling usually begins from identifying application queries and designing the data model to efficiently answer them so each database is designed to meet requirements of just one or more applications. But this practice causes a strong coupling between the data model and application queries and promotes data silos. Newly developed applications that manipulate connected data, usually stored in NoSQL Graph Databases, suffer from this type of problem, which is a challenge for data integration projects in Big Data scenarios. This systematic literature review (SLR) was carried out to identify the known approaches for data modeling of connected data. The main contribution of this SLR is an analysis of sixteen works, from 2013 to 2020, in terms of three dimensions: type of contribution, bibliometrics, and data modeling characteristics. Through this analysis, it was possible to identify that reverse engineering of connected data is still a research opportunity since few and incomplete works were found.

Stefano Lambiase, Gemma Catolino, Fabio Palomba et al.

Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the users, they are also known as conversational agents. Bots, particularly in their conversation-oriented version and AI-powered, have seen their adoption increase over time for software development and engineering purposes. Despite their exciting potential, ulteriorly enhanced by the advent of Generative AI and Large Language Models, bots still need to be improved to develop and integrate into the development cycle since practitioners report that bots add additional challenges that may worsen rather than improve. In this work, we aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption for Software Engineering associated with potential mitigation strategies. To reach our objectives, we conducted a multivocal literature review, reviewing both research and practitioner's literature. Through such an approach, we hope to contribute to both researchers and practitioners by providing first, a series of future research routes to follow, second, a list of strategies to adopt for improving the use of bots for software engineering purposes, and third, enforce a technology and knowledge transfer from the research field to the practitioners one, that is one of the primary goal of multivocal literature reviews.

Ylva Høgset, Marja Van den Heuvel-Panhuizen, Knut Berg

In the large body of literature that is available about the role of motivation in mathematics education, the focus is mostly on students’ attitudes and feelings toward mathematics in general. In the current small-scale explorative study, we zoomed in on what students think about the tasks they can come across in mathematics education. To investigate students’ appraisement of mathematics tasks, that is, their judgment of how much they like mathematics tasks, we used a mixed method approach with an online questionnaire including closed and open questions. We asked 67 Norwegian eight graders to give an appraisal score for 24 tasks and to express in their own words their reasons for liking/disliking them. In addition, the students also had to indicate for each task whether they think they can solve the tasks. The analysis of the data indicated that the students may prefer bare number problems over context problems. Similarly, students inclined to like puzzle-like tasks more than estimation or straightforward tasks. The reasons for liking/disliking a task most often refer to the difficulty or easiness of the task. The data about the perceived solvability revealed that the more the students believed that they can solve the task, the more they liked the task. Given the different appraisals that the students granted to the different task types and the comments they came with, there is much reason to afford students a stronger voice in mathematics education research, particularly when it is about the tasks used in mathematics education.

Maria Kristiansen, Abirami Srivarathan, Andrea Nedergaard Jensen

Objectives There is a need for novel approaches to address the complexity of social inequality in health. Public–private partnerships (PPPs) have been proposed as a promising approach; however, knowledge on lessons learnt from such partnerships remain unclear. This study synthesises evidence on opportunities and challenges of PPPs focusing on social inequality in health in upper-middle-income and high-income countries.Design A systematic literature review and meta-synthesis was conducted using the Mixed Methods Appraisal Tool for quality appraisal.Data sources PubMed, PsychInfo, Embase, Sociological Abstracts and SocIndex were searched for studies published between January 2013 and January 2023.Eligibility criteria Studies were eligible if they applied a quantitative, qualitative, or mixed methods design and reported on lessons learnt from PPPs focusing on social inequality in health in upper-middle-income and high-income countries. Studies had to be published in either English, Danish, German, Norwegian or Swedish.Data extraction and synthesis Two independent reviewers extracted data and appraised the quality of the included studies. A meta-synthesis with a descriptive intent was conducted and data were grouped into opportunities and challenges.Results A total of 16 studies of varying methodological quality were included. Opportunities covered three themes: (1) creating synergies, (2) clear communication and coordination, and (3) trust to sustain partnerships. Challenges were identified as reflected in the following three themes: (1) scarce resources, (2) inadequate communication and coordination, and (3) concerns on distrust and conflicting interest.Conclusions Partnerships across public, private and academic institutions hold the potential to address social inequality in health. Nevertheless, a variety of important lessons learnt are identified in the scientific literature. For future PPPs to be successful, partners should be aware of the availability of resources, provide clear communication and coordination, and address concerns on distrust and conflicting interests among partners.PROSPERO registration number CRD42023384608.

Richard E. Matland, Donley T. Studlar

I. Jawad, I. Lukšić, S. Rafnsson

Objective Sepsis is a complex and hard-to-define condition with many different interactions with other disorders. Presently, there are no estimates of the burden of sepsis and septicaemia at the global level and it was not included in the initial Global Burden of Disease study. Non-maternal sepsis has only recently received attention as a substantial global public health problem. The aim of this study was to assess available data on the burden of non-maternal sepsis, severe sepsis and septic shock in the community and to identify key gaps in information needed to estimate the global burden of sepsis. Methods Literature review of English language-based studies reporting on the incidence, prevalence, mortality or case-fatality of sepsis, severe sepsis and septic shock. The available literature was searched using the MEDLINE database of citations and abstracts of biomedical research articles published between 1980 and 2008. Findings 8 studies reported incidence of sepsis, severe sepsis or septic shock at the national level (4 from the USA and 1 from Brazil, the UK, Norway and Australia). No studies on the incidence, prevalence, mortality or case-fatality from sepsis in developing countries were found. The population sepsis incidence ranged from 22 to 240/100 000 (most plausible estimates ranged from 149 to 240/100 000); of severe sepsis from 13 to 300/100 000 (most of the estimates were between 56 and 91/100 000); and of septic shock 11/100 000. Case-fatality rate depends on the setting and severity of disease. It can reach up to 30% for sepsis, 50% for severe sepsis and 80% for septic shock. While the data were compiled using strict inclusion and exclusion criteria, a degree of uncertainty still exists regarding the reported estimates. Conclusion The few national-level reports available allow only a very crude estimation of the incidence of sepsis in developed countries while there is apparent lack of data from developing countries. A clear and universal definition of sepsis as well as the development of a sound epidemiological framework to begin addressing the magnitude of this problem is urgently needed through research in developing countries.

Carolin Gabbert, Susen Schaake, Theresa Lüth et al.

Abstract Background GBA1 variants are the strongest genetic risk factor for Parkinson’s disease (PD). However, the pathogenicity of GBA1 variants concerning PD is still not fully understood. Additionally, the frequency of GBA1 variants varies widely across populations. Objectives To evaluate Oxford Nanopore sequencing as a strategy, to determine the frequency of GBA1 variants in Norwegian PD patients and controls, and to review the current literature on newly identified variants that add to pathogenicity determination. Methods We included 462 Norwegian PD patients and 367 healthy controls. We sequenced the full-length GBA1 gene on the Oxford Nanopore GridION as an 8.9 kb amplicon. Six analysis pipelines were compared using two aligners (NGMLR, Minimap2) and three variant callers (BCFtools, Clair3, Pepper-Margin-Deepvariant). Confirmation of GBA1 variants was performed by Sanger sequencing and the pathogenicity of variants was evaluated. Results We found 95.8% (115/120) true-positive GBA1 variant calls, while 4.2% (5/120) variant calls were false-positive, with the NGMLR/Minimap2-BCFtools pipeline performing best. In total, 13 rare GBA1 variants were detected: two were predicted to be (likely) pathogenic and eleven were of uncertain significance. The odds of carrying one of the two common GBA1 variants, p.L483P or p.N409S, in PD patients were estimated to be 4.11 times the odds of carrying one of these variants in controls (OR = 4.11 [1.39, 12.12]). Conclusions In conclusion, we have demonstrated that Oxford long-read Nanopore sequencing, along with the NGMLR/Minimap2-BCFtools pipeline is an effective tool to investigate GBA1 variants. Further studies on the pathogenicity of GBA1 variants are needed to assess their effect on PD.

Henrik Torjusen

Hanna Dahls roman Kraft (2021) fortæller historien om Olav, som er tidligere medlem af NS og frivillig på Østfronten under 2. verdenskrig. Dahls roman er en af de første førstepersonsfortællinger i norsk litteratur om en overgriber, selvom romanen ikke beskriver de faktiske overgreb. I stedet undersøger romanen Olavs tab af værdighed som følge af hans dom for forræderi efter krigen. Tabet af værdighed er et centralt omdrejningspunkt, som kommer til at dominere Olavs idé om identitet og personlige relationer. I denne forstand forsøger Dahls repræsentation af Olav at undersøge, hvordan 2. verdenskrigs lange eftervirkninger i Norge ikke kun har påvirket ofrenes liv, men også overgribernes liv. Gennem at undersøge, hvordan romanen diskutere arkivets funktioner, spørgsmål om værdighed og narrativ empati, forsøger denne artikel at se nærmere romanens portræt af en overgriber, der forsøger at finde en måde at fortælle sin historie og genvinde sin værdighed i kamp med det omgivende samfund.

P. John