This paper provides a comprehensive literature review on the belief in false information, including misinformation, disinformation, and fake information. It addresses the increasing societal concern regarding false information, which is fueled by technological progress, especially advancements in artificial intelligence. This review systematically identifies and categorizes factors that influence the belief in false information. The review identifies 24 influence factors grouped into six main categories: demographic factors, personality traits, psychological factors, policy and values, media consumption, and preventive factors. Key findings highlight that lower education levels, high extraversion, low agreeableness, high neuroticism, and low cognitive reflection significantly increase belief in false information. The effectiveness of preventive strategies like labeling false information and promoting reflection about correctness is also discussed. This literature review conceptualizes belief in false information as a human-centered security risk in sociotechnical systems, as it can be exploited to manipulate decisions, undermine trust, and increase susceptibility to social engineering. It aims to inform preventive strategies that strengthen socio-technical security and societal resilience.
Abstract Aggregative behaviour is an important strategy for many fish species, especially for chondrichthyans that depend on close physical interactions between male and female conspecifics for mating and internal fertilization to occur. The Vulnerable spiny dogfish (Squalus acanthias Linnaeus, 1758), a temperate water demersal shark, is known for its aggregative behaviour where individuals tend to move in groups based on sex and/or size. Investigating their aggregative behaviour can be a tool to identify life history events such as mating, which is important for species management and habitat protection. We combined acoustic telemetry with network analysis to investigate co-occurrence aggregations among spiny dogfish in a complex fjord network in western Norway between October 2022 and April 2024. Data from 74 sharks tagged with acoustic transmitters equipped with depth sensors were analysed to conduct a four-dimensional co-occurrence network analysis. The analysis revealed that spiny dogfish showed a high centrality degree, interacting with many conspecifics, whereby male-male interactions were strongest. Time had similar effect on interaction patterns for the three interaction types (male-male, female-female, and female-male), while depth had a positive effect on same-sex interactions and a stronger effect on female-male interactions. There was a higher number of interactions between males and females in August and September, and in more confined spatial areas than in other months, potentially indicating mating periods and areas used by spiny dogfish. Protecting spiny dogfish populations, especially when and where mating occurs, is crucial for ensuring sustainable populations and appropriate management measures to prevent future dramatic population declines.
Feyi Adesanya, Kanan Castro Silva, Valdemar V. Graciano Neto
et al.
Modern systems exhibit unprecedented complexity due to their increased scale, interconnectedness, and the heterogeneity of their digital and physical components. In response to scaling challenges, the system of systems paradigm proposes flexible aggregations of subsystems into a larger whole, while maintaining the independence of subsystems to various degrees. In response to the cyber-physical convergence, the digital twin paradigm proposes a tight coupling between digital and physical components through computational reflection and precise control. As these two paradigms address distinct parts of the overall challenge, combining the two promises more comprehensive methods to engineer what we call systems of twinned systems. The noticeably growing body of knowledge on systems of twinned systems calls for a review of the state of the art. In this work, we report on our systematic literature survey of systems of twinned systems. We screened over 2,500 potential studies, of which we included 80 and investigated them in detail. To converge system of systems and digital twins, we derive a classification framework for systems of twinned systems that is backward compatible with the currently accepted theories of system of systems and digital twins.
Multi-agent large language models (MA-LLMs) are a rapidly growing research area that leverages multiple interacting language agents to tackle complex tasks, outperforming single-agent large language models. This literature review synthesizes the latest research on agent profiles, communication structures, and decision-making processes, drawing insights from both traditional multi-agent systems and state-of-the-art MA-LLM studies. In doing so, it aims to address the lack of direct comparisons in the field, illustrating how factors like scalability, communication structure, and decision-making processes influence MA-LLM performance. By examining frequent practices and outlining current challenges, the review reveals that multi-agent approaches can yield superior results but also face elevated computational costs and under-explored challenges unique to MA-LLM. Overall, these findings provide researchers and practitioners with a roadmap for developing robust and efficient multi-agent AI solutions.
Blockchain technology has rapidly expanded beyond its original use in cryptocurrencies to a broad range of applications, creating vast amounts of immutable, decentralized data. As blockchain adoption grows, so does the need for advanced data analytics techniques to extract insights for business intelligence, fraud detection, financial analysis and many more. While previous research has examined specific aspects of blockchain data analytics, such as transaction patterns, illegal activity detection, and data management, there remains a lack of comprehensive reviews that explore the full scope of blockchain data analytics. This study addresses this gap through a scoping literature review, systematically mapping the existing research landscape, identifying key topics, and highlighting emerging trends. Using established methodologies for literature reviews, we analyze 466 publications, clustering them into six major research themes: illegal activity detection, data management, financial analysis, user analysis, community detection, and mining analysis. Our findings reveal a strong focus on detecting illicit activities and financial applications, while holistic business intelligence use cases remain underexplored. This review provides a structured overview of blockchain data analytics, identifying research gaps and proposing future directions to enhance the fields impact.
In this article, we present a study of how teachers in secondary education discuss, negotiate and legitimise what grade to award students when they co-assess oral exams. The study draws on video recordings of authentic non-standardised oral exams in Norwegian language and literature. The oral exams involved pairs of examiners: the students’ teacher (the internal examiner), and a teacher from another school (the external examiner). The analysis reveals that oral exams are flexible enough to enable cooperation without complete agreement and thereby function as boundary objects, allowing internal examiners to advocate for their students while also complying with regulations. External examiners take the role of judge by having the last word about the grade. The findings of the study indicate that the practice of oral exams is guided by both formal regulations and established practices. We discuss the need for regulations that build on newer understandings of how examiner roles affect practice as this has implications for validity.
BackgroundEfficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true’ incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting.MethodsWithin the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens.ResultsMFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific.ConclusionsWhen routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence.
Fernando Gabriela Garcia, Spencer Burns, Harrison Fuller
In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail to provide deep comparative insights. ChatCite addresses this limitation by incorporating a multi-step reasoning mechanism that extracts critical elements from papers, incrementally builds a comparative summary, and refines the output through a reflective memory process. We evaluate ChatCite on a custom dataset, CompLit-LongContext, consisting of 1000 research papers with annotated comparative summaries. Experimental results show that ChatCite outperforms several baseline methods, including GPT-4, BART, T5, and CoT, across various automatic evaluation metrics such as ROUGE and the newly proposed G-Score. Human evaluation further confirms that ChatCite generates more coherent, insightful, and fluent summaries compared to these baseline models. Our method provides a significant advancement in automatic literature review generation, offering researchers a powerful tool for efficiently comparing and synthesizing scientific research.
Nicolás E. Díaz Ferreyra, Sirine Khelifi, Nalin Arachchilage
et al.
Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.
The article develops a new framework for the study of market reforms in Nordic welfare states based on a division between “markets”, “quasi-markets” and “pseudo-markets”. The two latter types of marketization have been the most common, and the article exemplifies them by revisiting the early 1990s Swedish school reform, “Friskolereformen”—which instigated a quasi-market for publicly funded schools run by both for-profit companies and non-profit actors—and the Norwegian hospital reform, “Foretaksreformen” of 2001—which created what we call a pseudo-market, in which public hospitals were reorganized to mimic the structures of capitalist enterprise. By discussing the different reforms in relation to justification, the type of welfare state sector, and the political orientation of the government implementing the reform, our study sheds new light on similarities and differences in marketization processes in the Nordics. Particularly, we find that the justification for the reforms differed, with the Swedish reform being justified in ideological terms and the Norwegian in technocratic terms. Contrary to some literature, we hold that marketization has fundamentally altered Nordic welfare states and the relationship between capital and society in the Nordics, and we suggest that our framework could be used for future comparative studies of market reforms.
Social pathology. Social and public welfare. Criminology
Maritime transport faces increasing pressure to reduce its greenhouse gas emissions to be in accordance with the Paris Agreement. For this to happen, low- and zero-carbon energy solutions need to be developed. In this paper we draw on sustainability transition literature and introduce the technological innovation system (TIS) framework to the field of maritime transportation research. The TIS approach analytically distinguishes between different innovation system functions that are important for new technologies to develop and diffuse beyond an early phase of experimentation. This provides a basis for technology-specific policy recommendations. We apply the TIS framework to the case of battery-electric and hydrogen energy solutions for coastal maritime transport in Norway. Whereas both battery-electric and hydrogen solutions have developed rapidly, the former is more mature and has a strong momentum. Public procurement and other policy instruments have been crucial for developments to date and will be important for these technologies to become viable options for shipping more generally.
Renewable energy plays an important role in the transition to a low emission society, yet in many regions energy projects have resulted in increasing societal polarization. Based on a comprehensive literature review and a survey among stakeholders from specific regions in Germany, Italy, Latvia, Norway, Poland and Spain with little prior experience with wind energy, we highlight six categories of factors that shape community acceptance of onshore wind energy development: technical characteristics of wind energy projects, environmental impacts, economic impacts, societal impacts, contextual factors and individual characteristics. We identify key similarities in acceptance-related patterns of wind energy development across the selected regions, but also important differences, highlighting the very context-specific nature of community acceptance. The findings contribute to improving the understanding of the forces, factors and relationships at play between policy frameworks and perceptions of wind energy under different conditions. We conclude by proposing policy recommendations regarding measures to increase the positive impacts and reduce the negative impacts of wind energy projects, and to strengthen existing drivers and reduce barriers to community acceptance of wind energy development.
The purpose of the study on which this paper is based was to conduct a performance evaluation of student learning for an introductory course in project management in a higher educational institution in Norway. This was done by utilizing performance measurement philosophy to evaluate perceived student learning after a project-based assignment was applied as an instructional tool. The evaluation was conducted at the end of the semester to determine whether it facilitated learning effectiveness by providing an authentic learning experience. Relevant learning criteria were identified from existing literature and were measured by means of a questionnaire survey. Ten measurement scales were established using a 5-point Likert scale. The survey was then rolled out for the same subject for two consecutive semesters for just over 100 project management students. The results indicated that the incorporation of project-based assignments has a positive impact on student learning, motivation, and performance both in the short and long term. The study finally revealed that the incorporation of project-based assignments enables the creation of real-life experiences, which further stimulates the creation and development of real-life competencies.
Eman Abdullah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni
Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as "Swiss army knife" of refactorings, as developers often apply it to improve their code quality. In recent years, several studies attempted to recommend Extract Method refactorings allowing the collection, analysis, and revelation of actionable data-driven insights about refactoring practices within software projects. In this paper, we aim at reviewing the current body of knowledge on existing Extract Method refactoring research and explore their limitations and potential improvement opportunities for future research efforts. Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. We review the body of knowledge related to Extract Method refactoring in the form of a systematic literature review (SLR). After compiling an initial pool of 1,367 papers, we conducted a systematic selection and our final pool included 83 primary studies. We define three sets of research questions and systematically develop and refine a classification schema based on several criteria including their methodology, applicability, and degree of automation. The results construct a catalog of 83 Extract Method approaches indicating that several techniques have been proposed in the literature. Our results show that: (i) 38.6% of Extract Method refactoring studies primarily focus on addressing code clones; (ii) Several of the Extract Method tools incorporate the developer's involvement in the decision-making process when applying the method extraction, and (iii) the existing benchmarks are heterogeneous and do not contain the same type of information, making standardizing them for the purpose of benchmarking difficult.
Mohammad Dehghani, Behrouz Bokharaeian, Zahra Yazdanparast
Scientific literature contains a considerable amount of information that provides an excellent opportunity for developing text mining methods to extract biomedical relationships. An important type of information is the relationship between singular nucleotide polymorphisms (SNP) and traits. In this paper, we present a BioBERT-GRU method to identify SNP- traits associations. Based on the evaluation of our method on the SNPPhenA dataset, it is concluded that this new method performs better than previous machine learning and deep learning based methods. BioBERT-GRU achieved the result a precision of 0.883, recall of 0.882 and F1-score of 0.881.