Aggregating experimental data from papers enables materials scientists to build better property prediction models and to facilitate scientific discovery. Recently, interest has grown in extracting not only single material properties but also entire experimental measurements. To support this shift, we introduce LitXBench, a framework for benchmarking methods that extract experiments from literature. We also present LitXAlloy, a dense benchmark comprising 1426 total measurements from 19 alloy papers. By storing the benchmark's entries as Python objects, rather than text-based formats such as CSV or JSON, we improve auditability and enable programmatic data validation. We find that frontier language models, such as Gemini 3.1 Pro Preview, outperform existing multi-turn extraction pipelines by up to 0.37 F1. Our results suggest that this performance gap arises because extraction pipelines associate measurements with compositions rather than the processing steps that define a material.
Metal-organic framework (MOF) databases have grown rapidly through experimental deposition and large-scale literature extraction, but recent analyses show that nearly half of their entries contain substantial structural errors. These inaccuracies propagate through high-throughput screening and machine-learning workflows, limiting the reliability of data-driven MOF discovery. Correcting such errors is exceptionally difficult because true repairs require integrating crystallographic files, synthesis descriptions, and contextual evidence scattered across the literature. Here we introduce LitMOF, a large language model-driven multi-agent framework that validates crystallographic information directly from the original literature and cross-validates it with database entries to repair structural errors. Applying LitMOF to the experimental MOF database (the CSD MOF Subset), we constructed LitMOF-DB, a curated set of 186,773 computation-ready structures, including the successful repair of 8,771 invalid entries, which accounts for 65.3% of the not-computation-ready MOFs in the latest CoRE MOF database. Additionally, the system uncovered 12,646 experimentally reported MOFs absent from existing resources, substantially expanding the known experimental design space. Using direct air capture screening as a case study, we demonstrate that structural errors severely distort predicted adsorption energies and CO2/H2O selectivity, leading to systematic misranking of materials, false positives, and the omission of high-performance candidates. This work establishes a scalable pathway toward self-correcting scientific databases and a generalizable paradigm for LLM-driven curation in materials science.
Eric O'Donoghue, Yvette Hastings, Ernesto Ortiz
et al.
Software Bill of Materials (SBOMs) are increasingly regarded as essential tools for securing software supply chains (SSCs), yet their real-world use and adoption barriers remain poorly understood. This systematic literature review synthesizes evidence from 40 peer-reviewed studies to evaluate how SBOMs are currently used to bolster SSC security. We identify five primary application areas: vulnerability management, transparency, component assessment, risk assessment, and SSC integrity. Despite clear promise, adoption is hindered by significant barriers: generation tooling, data privacy, format/standardization, sharing/distribution, cost/overhead, vulnerability exploitability, maintenance, analysis tooling, false positives, hidden packages, and tampering. To structure our analysis, we map these barriers to the ISO/IEC 25019:2023 Quality-in-Use model, revealing critical deficiencies in SBOM trustworthiness, usability, and suitability for security tasks. We also highlight key gaps in the literature. These include the absence of applying machine learning techniques to assess SBOMs and limited evaluation of SBOMs and SSCs using software quality assurance techniques. Our findings provide actionable insights for researchers, tool developers, and practitioners seeking to advance SBOM-driven SSC security and lay a foundation for future work at the intersection of SSC assurance, automation, and empirical software engineering.
The implementation of transformational pedagogy in secondary education classrooms requires a broad multiliteracy approach. Due to limited planning time and resources, high school English Literature teachers often struggle to curate diverse, thematically aligned literature text sets. This study addresses the critical need for a tool that provides scaffolds for novice educators in selecting literature texts that are diverse -- in terms of genre, theme, subtheme, and author -- yet similar in context and pedagogical merits. We have developed a recommendation system, Teaching Text Expansion for Teacher Scaffolding (T-TExTS), that suggests high school English Literature books based on pedagogical merits, genre, and thematic relevance using a knowledge graph. We constructed a domain-specific ontology using the KNowledge Acquisition and Representation Methodology (KNARM), transformed into a knowledge graph, which was then embedded using DeepWalk, biased random walk, and a hybrid of both approaches. The system was evaluated using link prediction and recommendation performance metrics, including Area Under the Curve (AUC), Mean Reciprocal Rank (MRR), Hits@K, and normalized Discounted Cumulative Gain (nDCG). DeepWalk outperformed in most ranking metrics, with the highest AUC (0.9431), whereas the hybrid model offered balanced performance. These findings demonstrate the importance of semantic, ontology-driven approaches in recommendation systems and suggest that T-TExTS can significantly ease the burden of English Literature text selection for high school educators, promoting more informed and inclusive curricular decisions. The source code for T-TExTS is available at: https://github.com/koncordantlab/TTExTS
Chronic diseases are long-term, manageable, yet typically incurable conditions, highlighting the need for effective preventive strategies. Machine learning has been widely used to assess individual risk for chronic diseases. However, many models rely on medical test data (e.g. blood results, glucose levels), which limits their utility for proactive self-assessment. Additionally, to gain public trust, machine learning models should be explainable and transparent. Although some research on self-assessment machine learning models includes explainability, their explanations are not validated against established medical literature, reducing confidence in their reliability. To address these issues, we develop deep learning models that predict the risk of developing 13 chronic diseases using only personal and lifestyle factors, enabling accessible, self-directed preventive care. Importantly, we use SHAP-based explainability to identify the most influential model features and validate them against established medical literature. Our results show a strong alignment between the models' most influential features and established medical literature, reinforcing the models' trustworthiness. Critically, we find that this observation holds across 13 distinct diseases, indicating that this machine learning approach can be broadly trusted for chronic disease prediction. This work lays the foundation for developing trustworthy machine learning tools for self-directed preventive care. Future research can explore other approaches for models' trustworthiness and discuss how the models can be used ethically and responsibly.
As frontier AI systems advance toward transformative capabilities, we need a parallel transformation in how we measure and evaluate these systems to ensure safety and inform governance. While benchmarks have been the primary method for estimating model capabilities, they often fail to establish true upper bounds or predict deployment behavior. This literature review consolidates the rapidly evolving field of AI safety evaluations, proposing a systematic taxonomy around three dimensions: what properties we measure, how we measure them, and how these measurements integrate into frameworks. We show how evaluations go beyond benchmarks by measuring what models can do when pushed to the limit (capabilities), the behavioral tendencies exhibited by default (propensities), and whether our safety measures remain effective even when faced with subversive adversarial AI (control). These properties are measured through behavioral techniques like scaffolding, red teaming and supervised fine-tuning, alongside internal techniques such as representation analysis and mechanistic interpretability. We provide deeper explanations of some safety-critical capabilities like cybersecurity exploitation, deception, autonomous replication, and situational awareness, alongside concerning propensities like power-seeking and scheming. The review explores how these evaluation methods integrate into governance frameworks to translate results into concrete development decisions. We also highlight challenges to safety evaluations - proving absence of capabilities, potential model sandbagging, and incentives for "safetywashing" - while identifying promising research directions. By synthesizing scattered resources, this literature review aims to provide a central reference point for understanding AI safety evaluations.
Fengji Zhang, Zexian Zhang, Jacky Wai Keung
et al.
Code Smell Detection (CSD) plays a crucial role in improving software quality and maintainability. And Deep Learning (DL) techniques have emerged as a promising approach for CSD due to their superior performance. However, the effectiveness of DL-based CSD methods heavily relies on the quality of the training data. Despite its importance, little attention has been paid to analyzing the data preparation process. This systematic literature review analyzes the data preparation techniques used in DL-based CSD methods. We identify 36 relevant papers published by December 2023 and provide a thorough analysis of the critical considerations in constructing CSD datasets, including data requirements, collection, labeling, and cleaning. We also summarize seven primary challenges and corresponding solutions in the literature. Finally, we offer actionable recommendations for preparing and accessing high-quality CSD data, emphasizing the importance of data diversity, standardization, and accessibility. This survey provides valuable insights for researchers and practitioners to harness the full potential of DL techniques in CSD.
We present a scoping review of published literature on ethnomathematics and Indigenous mathematics as a step towards a goal to decolonize the prevailing Eurocentric view of the provenance of mathematics. Mathematical practices were identified globally from 169 included studies. We map three development stages of ethnomathematical research from 1984 to 2023 and identify 20 categories of Indigenous and traditional cultural activities that evidence mathematical design and expression. We address two challenges of investigating non-Western based mathematics: where to look for mathematical knowledge, and how to decode it from cultural practices. These two hurdles are overcome by cluster analysis of the keywords of included studies. Existing research falls into two categories: I. identification of mathematical concepts used in Indigenous societies, and II. systematizing identified mathematical concepts. Both approaches are essential for research on Indigenous mathematics to flourish, in order to empower Indigenous knowledge holders and deconstruct restrictive colonial boundaries of mathematical knowledge and education.
The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper addresses this gap by providing a systematic literature review, covering the analysis of over 300 works, encompassing 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three key research questions: the construction of cybersecurity-oriented LLMs, the application of LLMs to various cybersecurity tasks, the challenges and further research in this area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices and serve as a valuable resource for applying LLMs in this field. We also maintain and regularly update a list of practical guides on LLMs for cybersecurity at https://github.com/tmylla/Awesome-LLM4Cybersecurity.
Noticeably, Africa is underrepresented in the computing education research (CER) community. However, there has been some effort from the researchers in the region to contribute to the growing need for computing for all. To understand the body of works that emerged from the global south region and their area of focus in computing education, we conducted a systematic review of the literature. This research investigates the prominent CER journals and conferences to discern the kind of research that has been published and how much contribution they have made to the growing field. Of the 68 selected studies, 45 papers were from South Africa. The prominent aspect of computing in the literature is programming, which accounts for 43%. We identified open areas for research in the context and discussed the implication of our findings for the development of CER in Africa.
I artikelns första del diskuteras självmord som motiv och tema i skräcklitteratur sedan medeltiden och framåt och mytologiseringen av verkliga skräckförfattare tas upp. I dess andra del görs en analys av gentlemännens självmord i Machens The Great God Pan utifrån tolkningen att självmorden med snara har en (grekisk) antik koppling och är feminint kodade. Slutligen görs i del 3 en läsning av en trolig intertext kring mystik och (skräck)litterature från E.A. Poes idé i ”The Poetic Principle” om att sann poesi är ett uttryck för den odödliga själens längtan till, och reminiscenser av, livet efter döden, via skönhetsupplevelser i verkliga livet, över Lovecraft, som via snarlika formuleringar förbinder skönheten i bland annat skräcklitteratur med sina upplevelser av arkitektur och ett minne av att han har levt i en stad före universum skapades. Lovecrafts beskrivning återfinns snarlikt i novellen ”The Night Ocean” som skrevs av Lovecrafts litteräre exekutor R.H. Barlow, och jag läser det som att Barlow använder idén på ett nytt sätt, med mer fysiskt kroppslig inriktning och kanske ett homosexuellt självbiografiskt tema.
Norway is an excellent case for studying the ambiguities and competing interests attached to climate change and global sustainability. The country is at the forefront in the use of “clean energy,” yet has long been one of the world’s largest oil exporters and its dominant ideas have been challenged given the climate-change crisis. The article offers a preliminary review of the historical and current connections that exist between the development of national welfare states in Europe (and Norway in particular) and the long-standing practice of extractivist efforts that have harmed nature in indigenous lands within Norwegian borders and colonized, or previously colonized lands, outside Europe). As a comparative exercise, this article mobilizes the idea of social imaginaries to conceptualize and explore how the Norwegian welfare state has, in the social policy literature and in public discourse, been linked to the notion of sustainability, given the context of rapid climate change. By drawing on three illustrative cases, the article demonstrates how such imaginaries are present in current discussions. It suggests that unpacking such imaginaries within a historical and relational global view may contribute to understanding the role of nationalism and colonialism in Norway’s sustainability narratives, to potentially trigger change in the way we conceptualize welfare-state sustainability.
This article analyzes Nordahl Grieg’s Skibet gaar videre (1924) as a maritime novel from the age of steamships. It explains the novel’s “maritime world picture” (Søren Frank) as a kind of disenchantment connected to the steamship. In addition, it analyzes the novel’s moments of re-enchantment that are connected to sailing ships and sea animals. The article is structured around several genre-typical chronotopes of sea adventure narrative, which help to explain the young Grieg’s aesthetic and ideological relations to the novel of the sea.
Hvordan kan vi forstå Bjarte Breiteigs novelle «Der nede sørger de ikke»? Novellen er en realistisk skildring av to ungdommer som tilsynelatende uten grunn raserer formingssalene på skolen. På et symbolsk nivå er hærverket imidlertid knyttet til forestillinger om selvmord, kreativitet og individualitet.
Michail N. Giannakos, Patrick Mikalef, Ilias O. Pappas
AbstractE-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that result in rich learning flows within organizations. The purpose of this article is to provide a systematic review of empirical studies at the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty-seven peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions of the research on the confluence of e-learning and organizational learning during the last decade. Future research should leverage big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increase organizational value.
As the number of empirical studies increases unprecedentedly in line with expansions in higher education, theoretical developments in population studies suffer due to a discoverability crisis of related work. The systematic use of previous research is common in the medical sciences through various types of structured reviews. However, these are less common in the social sciences, despite their potential, especially in cross-disciplinary fields such as migration. We use Google Scholar to examine the niche of housing research within migration studies through a broad range of documents. The contribution of this meta-analysis is threefold. Firstly, we illustrate the association of keywords across the corpus of literature related to migration and housing and map the growth of migration literature since the 1960s. Secondly, we highlight key bridging documents using network measures. Finally, we estimate the distance in reading time between documents. Our findings suggest that the corpus of previous work at the intersection of migration and housing consists of many additive case studies, indicating a gap in integrative approaches replicable across a growing knowledge landscape.
Deepfake videos are defined as a resulting media from the synthesis of different persons images and videos, mostly faces, replacing a real one. The easy spread of such videos leads to elevated misinformation and represents a threat to society and democracy today. The present study aims to collect and analyze the relevant literature through a systematic procedure. We present 27 articles from scientific databases revealing threats to society, democracies, the political life but present as well advantages of this technology in entertainment, gaming, education, and public life. The research indicates high scientific interest in deepfake detection algorithms as well as the ethical aspect of such technology. This article covers the scientific gap since, to the best of our knowledge, this is the first systematic literature review in the field. A discussion has already started among academics and practitioners concerning the spread of fake news. The next step of fake news considers the use of artificial intelligence and machine learning algorithms that create hyper-realistic videos, called deepfake. Deepfake technology has continuously attracted the attention of scholars over the last 3 years more and more. The importance of conducting research in this field derives from the necessity to understand the theory. The first contextual approach is related to the epistemological points of view of the concept. The second one is related to the phenomenological disadvantages of the field. Despite that, the authors will try to focus not only on the disadvantages of the field but also on the positive aspects of the technology.
Blockchain has been proposed to facilitate the enactment of interorganisational business processes. For such processes, blockchain can guarantee the enforcement of rules and the integrity of execution traces - without the need for a centralised trusted party. However, the enactment of interorganisational processes pose manifold challenges. In this work, we ask what answers the research field offers in response to those challenges. To do so, we conduct a systematic literature review (SLR). As our guiding question, we investigate the guarantees and capabilities of blockchain-based enactment approaches. Based on resulting empirical evidence, we develop a taxonomy for blockchain-based enactment. We find that a wide range of approaches support traceability and correctness; however, research focusing on flexibility and scalability remains nascent. For all challenges, we point towards future research opportunities.
Rosa Cheesman, Espen Moen Eilertsen, Yasmin I. Ahmadzadeh
et al.
Abstract Background Many studies detect associations between parent behaviour and child symptoms of anxiety and depression. Despite knowledge that anxiety and depression are influenced by a complex interplay of genetic and environmental risk factors, most studies do not account for shared familial genetic risk. Quantitative genetic designs provide a means of controlling for shared genetics, but rely on observed putative exposure variables, and require data from highly specific family structures. Methods The intergenerational genomic method, Relatedness Disequilibrium Regression (RDR), indexes environmental effects of parents on child traits using measured genotypes. RDR estimates how much the parent genome influences the child indirectly via the environment, over and above effects of genetic factors acting directly in the child. This ‘genetic nurture’ effect is agnostic to parent phenotype and captures unmeasured heritable parent behaviours. We applied RDR in a sample of 11,598 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) to estimate parental genetic nurture separately from direct child genetic effects on anxiety and depression symptoms at age 8. We tested for mediation of genetic nurture via maternal anxiety and depression symptoms. Results were compared to a complementary non-genomic pedigree model. Results Parental genetic nurture explained 14% of the variance in depression symptoms at age 8. Subsequent analyses suggested that maternal anxiety and depression partially mediated this effect. The genetic nurture effect was mirrored by the finding of family environmental influence in our pedigree model. In contrast, variance in anxiety symptoms was not significantly influenced by common genetic variation in children or parents, despite a moderate pedigree heritability. Conclusions Genomic methods like RDR represent new opportunities for genetically sensitive family research on complex human traits, which until now has been largely confined to adoption, twin and other pedigree designs. Our results are relevant to debates about the role of parents in the development of anxiety and depression in children, and possibly where to intervene to reduce problems.
ABSTRACT Teacher quality, or ‘the good teacher’ is not clearly defined in Norway, nor are there specific standards for measuring teacher quality. Everybody has an opinion about the good teacher, and teacher quality is frequently debated. Moreover, in Norway there is no systematic evaluation of teachers. Nevertheless, numerous reforms and popular discourses indirectly revisit and revise the formal qualification competences of teachers. This paper is an explorative journey into research, fiction and policy documents searching for how teacher quality has been, and is, perceived in Norway. The paper discusses relevant research, presents a historical contextualisation, my interpretations of selected fiction literature, and policy documents. I argue that teacher education has the responsibility of not merely translating policies into practice, but also to act as a critical bridge-builder between academic and relational aspects of teaching. Teacher education should offer a research-informed, practice-relevant education of teachers for the current and future school.