Context: Change mining enables organizations to understand the changes that occurred in their business processes. This allows them to enhance their business processes and adapt to dynamic environments. Therefore, change mining is becoming a topic of interest for researchers, scholars, and practitioners. Objective: Motivated by the goal of establishing the state of the art in this area, this paper aims to investigate the literature in change logging and mining in process-aware information systems, provide an overview of the methods that are used in the existing publications, and identify gaps in the research on the topic of logging and mining process changes. Method: A literature review is conducted with the objective to identify and define methods to mine, store, and record changes in business processes. From 1136 publications, we selected 6 papers related to changes in business process and extended the list to 9 papers by including the relevant articles referenced by the papers that we selected originally. Results: In answer of our research questions, we have identified two classes of change mining methods, two ways of recording the changes into change logs, five formats for change log representation, and four objectives to be learned from changes. Conclusion: The literature review provides a summary of existing change mining and logging methods in process-aware information systems and identifies a number of research gaps in the area.
This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation (RAG) architecture, designed to improve access to accurate, evidence-based medical information. Addressing the shortcomings of conventional health search engines and the lag in public access to biomedical research, the system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias ,to retrieve relevant information and generate concise, context-aware responses. The retrieval pipeline uses MiniLM-based semantic embeddings and FAISS vector search, while answer generation is performed by a fine-tuned Mistral-7B-v0.3 language model optimized using QLoRA for efficient, low-resource training. The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature demonstrating the value of domain-aligned retrieval. Empirical results, measured using BERTScore (F1), show substantial improvements in factual consistency and semantic relevance compared to baseline models. The findings underscore the potential of RAG-enhanced language models to bridge the gap between complex biomedical literature and accessible public health knowledge, paving the way for future work on multilingual adaptation, privacy-preserving inference, and personalized medical AI systems.
Gregory Hok Tjoan Go, Khang Ly, Anders Søgaard
et al.
The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing.
We propose an automated pipeline for performing literature reviews using semantic similarity. Unlike traditional systematic review systems or optimization based methods, this work emphasizes minimal overhead and high relevance by using transformer based embeddings and cosine similarity. By providing a paper title and abstract, it generates relevant keywords, fetches relevant papers from open access repository, and ranks them based on their semantic closeness to the input. Three embedding models were evaluated. A statistical thresholding approach is then applied to filter relevant papers, enabling an effective literature review pipeline. Despite the absence of heuristic feedback or ground truth relevance labels, the proposed system shows promise as a scalable and practical tool for preliminary research and exploratory analysis.
Ved å se nærmere på innholdet i den muntlige avgangsprøven i norsk, belyser denne artikkelen skjønnlitteraturens plass i ungdomsskolens norskfag og hvilken litterær kompetanse som etterspørres til eksamen. Muntlig eksamen utformes lokalt på den enkelte skole, og vi har lite kunnskap om det faglige innholdet. Til denne studien er det samlet inn til sammen 50 eksamensoppgaver fra 16 skoler våren 2023. En tredel av oppgavene handler om litteratur, og ytterligere en tredel åpner for at elevene kan velge et litterært tema. Hovedfunn i undersøkelsen er (1) at det er få tegn til kanonisering av bestemte tekster, (2) at samtidstekster dominerer blant tekstene det refereres til, noen av dem med tematikk knyttet til det flerkulturelle Norge, (3) at lyrikken er nærmest fraværende, mens (4) filmtitler er like vanlig som romaner, (5) at oppgaveformuleringene er svært åpne og gir elevene stor valgfrihet, (6) at mange oppgaver i liten grad innbyr til at elevene kan vise evne til å sammenligne eller tolke konkrete tekster ettersom ingen av oppgavene inkluderer tekstvedlegg eller gir elevene klar instruks om å benytte bestemte tekster, og (7) at enkelte skoler gir oppgaver som ikke fremstår som norskfaglige.
English abstract
What Do the Tasks Given in the Oral Exam in the Norwegian Subject Tell Us About Fiction and Literary Competence in Lower Secondary School?
By closely examining the content of the final oral examination, this article sheds light on the role of fiction within the lower secondary school subject of Norwegian and the literary competence sought after in the exam. The oral exam is organized locally at each school, and we have limited knowledge about its academic content. For this study, a total of 50 exam tasks were collected from 16 schools in the spring of 2023. One-third of the tasks focus on literature, and an additional one-third allow students to choose a literary theme. The main findings of the study are that (1) there are few signs of canonization of specific texts, (2) contemporary texts dominate among the referenced titles, some of which address themes related to Norway as a multicultural society, (3) poetry is almost absent, while (4) film titles are as common as novels, (5) the task formulations are very open and provide students with significant freedom of choice, (6) many tasks do not encourage students to demonstrate the ability to compare or interpret specific texts, as none of the tasks include texts (or excerpts), nor do they clearly instruct students to use specific texts, and (7) some schools assign tasks that on surface do not seem to pertain to the Norwegian subject.
Education (General), Language. Linguistic theory. Comparative grammar
Biomedical literature is a rapidly expanding field of science and technology. Classification of biomedical texts is an essential part of biomedicine research, especially in the field of biology. This work proposes the fine-tuned DistilBERT, a methodology-specific, pre-trained generative classification language model for mining biomedicine texts. The model has proven its effectiveness in linguistic understanding capabilities and has reduced the size of BERT models by 40\% but by 60\% faster. The main objective of this project is to improve the model and assess the performance of the model compared to the non-fine-tuned model. We used DistilBert as a support model and pre-trained on a corpus of 32,000 abstracts and complete text articles; our results were impressive and surpassed those of traditional literature classification methods by using RNN or LSTM. Our aim is to integrate this highly specialised and specific model into different research industries.
Introduction: Thorough maintenance of the scientific record is needed to ensure the trustworthiness of its content. This can be undermined by a stealth correction, which is at least one post-publication change made to a scientific article, without providing a correction note or any other indicator that the publication was temporarily or permanently altered. In this paper we provide several examples of stealth corrections in order to demonstrate that these exist within the scientific literature. As far as we are aware, no documentation of such stealth corrections was previously reported in the scientific literature. Methods: We identified stealth corrections ourselves, or found already reported ones on the public database pubpeer.com or through social media accounts of known science sleuths. Results: In total we report 131 articles that were affected by stealth corrections and were published between 2005 and 2024. These stealth corrections were found among multiple publishers and scientific fields. Conclusion: and recommendations Stealth corrections exist in the scientific literature. This needs to end immediately as it threatens scientific integrity. We recommend the following: 1) Tracking all changes to the published record by all publishers in an open, uniform and transparent manner, preferably by online submission systems that log every change publicly, making stealth corrections impossible; 2) Clear definitions and guidelines on all types of corrections; 3) Support sustained vigilance of the scientific community to publicly register stealth corrections.
Citations allow quickly identifying related research. If multiple publications are selected as seeds, specific suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refines the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, first, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defined keywords, which reflect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the field. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks. These tasks span five core scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification. SciRIFF is unique in being entirely expert-written, high-quality instruction-following dataset for extracting and synthesizing information from research literature across diverse scientific fields. It features complex instructions with long input contexts, detailed task descriptions, and structured outputs. To demonstrate its utility, we finetune a series of large language models (LLMs) using a mix of general-domain and SciRIFF instructions. On nine out-of-distribution held-out tasks (referred to as SciRIFF-Eval), LLMs finetuned on SciRIFF achieve 70.6% average improvement over baselines trained only on general-domain instructions. SciRIFF facilitates the development and evaluation of LLMs to help researchers navigate the rapidly growing body of scientific literature.
This paper aims at a brief overview of the main impact of ChatGTP in the scientific field of programming and learning/education in computer science. It lists, covers and documents from the literature the major issues that have been identified for this topic, such as applications, advantages and limitations, ethical issues raised. Answers to the above questions were solicited from ChatGPT itself, the responses were collected, and then the recent literature was surveyed to determine whether or not the responses are supported. The paper ends with a short discussion on what is expected to happen in the near future. A future that can be extremely promising if humanity manages to have AI as a proper ally and partner, with distinct roles and specific rules of cooperation and interaction.
Waqas Muhammad Qazi, Simon Ballance, Katerina Kousoulaki
et al.
Cell wall disrupted and dried Microchloropsis gaditana (Mg), Tetraselmis chui (Tc) and Chlorella vulgaris (Cv) microalgae biomasses, with or without ethanol pre-treatment, were added to wheat bread at a wheat flour substitution level of 12%, to enrich bread protein by 30%. Baking performance, protein quality and basic sensory properties were assessed. Compared to wheat, Mg, Tc and Cv contain higher amounts of essential amino acids and their incorporation markedly improved protein quality in the bread (DIAAS 57–66 vs. 46%). The incorporation of microalgae reduced dough strength and bread volume and increased crumb firmness. This was most pronounced for Cv and Tc but could be improved by ethanol treatment. Mg gave adequate dough strength, bread volume and crumb structure without ethanol treatment. To obtain bread of acceptable smell, appearance, and colour, ethanol treatment was necessary also for Mg as it markedly reduced the unpleasant smell and intense colour of all algae breads. Ethanol treatment reduced the relative content of lysine, but no other essential amino acids. However, it also had a negative impact on in vitro protein digestibility. Our results show that Mg had the largest potential for protein fortification of bread, but further work is needed to optimize pre-processing and assess consumer acceptance.
There is a growing interest in understanding the energy and environmental footprint of digital currencies, specifically in cryptocurrencies such as Bitcoin and Ethereum. These cryptocurrencies are operated by a geographically distributed network of computing nodes, making it hard to accurately estimate their energy consumption. Existing studies, both in academia and industry, attempt to model the cryptocurrencies energy consumption often based on a number of assumptions for instance about the hardware in use or geographic distribution of the computing nodes. A number of these studies has already been widely criticized for their design choices and subsequent over or under-estimation of the energy use. In this study, we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain such as social energy sciences and information systems. We first design a quality assessment framework based on existing research, we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these issues. Our goal with this article is to to advance the field by promoting scientific rigor in studies focusing on Blockchain's energy footprint. To that end, we provide a novel set of codes of conduct for the five most widely used research methodologies: quantitative energy modeling, literature reviews, data analysis \& statistics, case studies, and experiments. We envision that these codes of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems' energy and environmental footprint.
Nina Dehnhard, Magdalene Langset, Asgeir Aglen
et al.
Abstract Piscivorous wildlife is often perceived as competitors by humans. Great cormorants of the continental subspecies (Phalacrocorax carbo sinensis) in the Baltic and North Sea increase, while local cod (Gadus morhua) stocks decline. In contrast, numbers of the Atlantic subspecies (Phalacrocorax carbo carbo), breeding along the Norwegian and Barents Seas, have been relatively stable. We investigated the diet of both great cormorant subspecies in breeding colonies along the Norwegian Coast from Lofoten to the Skagerrak and estimated the biomass of fish consumed annually by great cormorants in Norwegian waters. The birds’ consumption was compared with estimated fish stock sizes and fishery catches. Cod and saithe (Pollachius virens) dominated the diet in the Norwegian Sea and wrasses in the North Sea and Skagerrak. Estimated total fish consumption of cod and saithe by great cormorants was <1.7% of estimated fish stocks and <9% of that of human catches and therefore considered minor. Cormorant consumption of wrasses amounted to 110% of human catches. The practice of using wrasses as cleaner fish in the salmon farming industry leads to a conflict with cormorants, and we urge for a better understanding and management of wrasse populations, taking ecosystem functioning and natural predation into account.
Giorgia Minello, Carlo R. M. A. Santagiustina, Massimo Warglien
During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdown policy evaluation. Up to now, hundreds of thousands of papers have been uploaded on online repositories and published in scientific journals. As a result, the development of digital methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis to investigate topics under their surface. Specifically, LDA2Net exploits the frequencies of pairs of consecutive words to reconstruct the network structure of topics discussed in the Cord-19 corpus. The results suggest that the effectiveness of topic models can be magnified by enriching them with word network representations, and by using the latter to display, analyse, and explore COVID-related topics at different levels of granularity.
Francisco Ponce, Jacopo Soldani, Hernán Astudillo
et al.
Context: Securing microservice-based applications is crucial, as many IT companies are delivering their businesses through microservices. If security smells affect microservice-based applications, they can possibly suffer from security leaks and need to be refactored to mitigate the effects of security smells therein. Objective: As the currently available knowledge on securing microservices is scattered across different pieces of white and grey literature, our objective here is to distill well-known smells for securing microservices, together with the refactorings enabling to mitigate the effects of such smells. Method: To capture the state of the art and practice in securing microservices, we conducted a multivocal review of the existing white and grey literature on the topic. We systematically analyzed 58 studies published from 2014 until the end of 2020. Results: Ten bad smells for securing microservices are identified, which we organized in a taxonomy, associating each smell with the security properties it may violate and the refactorings enabling to mitigate its effects. Conclusions: The security smells and the corresponding refactorings have pragmatic value for practitioners, who can exploit them in their daily work on securing microservices. They also serve as a starting point for researchers wishing to establish new research directions on securing microservices.
A literature search indicates an absence of research into boy’s experiences of physical education (PE) in classes in which there is a significant majority of girls. The aim of the study was to examine how boys in such classes experience their PE lessons. The methodological approach was qualitative, and data were collected with interviews of 13 boys in classes with more than 90% girls at a Norwegian high school. The data were analyzed with QSR NVivo 10 (London), focused on creating categories of meaning, in which students’ experiences were taken as subjectively true. The data are based on subjective constructions, which students constructed as part of their own interpretations and reflections on what had occurred in PE at the school. Results of the study came out in the form of three main findings. Two of those relate to a negative experience and the third to a positive experience of PE. The boys mostly felt that they are physically superior and have to consider the girls. Furthermore, the boys reported little challenge and feelings of mastery while being together with passive girls who are allowed to choose the activities. However, the boys found it easier to show off in front of the teachers and classmates when there were just a few boys in the class. The results are discussed in relation to gender-related theory on how the respondents are producing a traditional male gender in PE through their mastery, strength, and ambition to compete. We suggest a new approach of teaching that is more student-centered. A strategy could be to include other activities than sport-based activities into PE – activities that do not require strength and other athletic skills leading to feelings of hegemonic masculinity. A larger focus on social interactions during PE classes – activities in which students’ sex is not as important as in traditional teacher- and sport-centered PE classes, may be a good strategy.
AbstractThe version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill.
Gullbrekken Lars, Schjøth Bunkholt Nora, Geving Stig
et al.
The harsh Norwegian climate requires buildings designed to high standards. An airtight building envelope is crucial to achieve an energy efficient building and to avoid moisture problems. Results from the SINTEF Building defects archive show that a considerable part of the building defects is related to air leakages. In addition, air leakages increase the energy demand of buildings. A literature study has been conducted in order to map typical air leakage paths of Norwegian wooden houses. In order to increase the performance, different sealing methods including the use of tape has been reviewed. The results show that the most common air leakages reported from field measurements in the literature are in the connections between external wall and ceiling or floor, external wall and window or door, and external wall and penetrations in the barrier layers. Results from laboratory investigations showed that the traditional solutions can be further improved by introduction of modern foil materials in combination with sealing tapes. However, questions can be raised regarding the necessity of tape sealing all available joints.
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.
Previous studies on perspective in spatial signed language descriptions suggest a basic dichotomy between either a route or a survey perspective, which entails either the signer being conceptualized as a mobile agent within a life-sized scene or the signer in a fixed position as an external observer of a scaled-down scene. We challenge this dichotomy by investigating the particular couplings of vantage point position and mobility engaged during various types of spatial language produced across eight naturalistic conversations in Norwegian Sign Language. Spatial language was annotated for the purpose of the segment, the size of the environment described, the signs produced, and the location and mobility of vantage points. Analysis revealed that survey and route perspectives, as characterized in the literature, do not adequately account for the range of vantage point combinations observed in conversations (e.g., external, but mobile, vantage points). There is also some preliminary evidence that the purpose of the spatial language and the size of the environments described may also play a role in how signers engage vantage points. Finally, the study underscores the importance of investigating spatial language within naturalistic conversational contexts.