Publikationer från Högskolan i Skövde
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Ore extensions of abelian groups with operators
No full textGiven a set A and an abelian group B with operators in A, in the sense of Krull and Noether, we introduce the Ore group extension B[x;\sigma_B,\delta_B] as the additive group B[x], with A[x] as a set of operators. Here, the action of A[x] on B[x] is defined by mimicking the multiplication used in the classical case where A and B are the same ring. We derive generalizations of Vandermonde's and Leibniz's identities for this construction, and they are then used to establish associativity criteria. Additionally, we prove a version of Hilbert's basis theorem for this structure, under the assumption that the action of A on B is what we call weakly s-unital. Finally, we apply these results to the case where B is a left module over a ring A, and specifically to the case where A and B coincide with a non-associative ring which is left distributive but not necessarily right distributive.CC BY 4.0© 2025 The Author(s)Corresponding author: [email protected] (P. Bäck)</p
AI vs. Humans : Comparing road user intention recognition performance
No full textAnticipating the behavior of other road users is critical for safe driving. To anticipate the behavior of other road users in a timely manner, it is essential to recognize their intentions. Although artificial intelligence (AI)-based intention recognition systems for traffic scenarios have advanced significantly, their performance relative to human road user intention recognition (RUIR) remains largely unexplored. To address this gap, we conducted an experiment comparing the RUIR performance of human participants and a state-of-the-art end-to-end video recognition AI model on a set of 25 video scenarios. The selected scenarios offered a balanced representation of various road user types and a range of intention maneuvers. Among human participants (N=161), we found no statistically significant differences in RUIR performance with respect to age, self-perceived driving skill, annual driven kilometers, or years of driving experience. However, the average human participant exhibited slightly lower RUIR performance than the AI models.CC BY 4.0Corresponding author: [email protected] (K. Vellenga)</p
Designing Synthetic Active Learning for model refinement in manufacturing parts detection
No full textThis paper introduces Synthetic Active Learning (SAL), a fully automatic model refinement strategy for manufacturing parts detection using only synthetic data actively generated with domain randomization for training. SAL iteratively updates the detection model by identifying its weaknesses, such as in specific categories, materials, or object sizes, using custom evaluators, and generating targeted synthetic data to address them; it selectively synthesizes new useful data with respect to active learning, where traditionally humans in the loop select data to label. During each iteration, model training and data generation occur simultaneously to improve efficiency. Evaluated on four use cases from two industrial datasets, SAL achieved mAP@50 improvements of 2 to 6% percentage points over static learning, which refers to training on a fixed, pre-generated dataset. It also showed notable gains in underperforming categories, leading to more balanced performance across classes. Another benefit is that it uses a consistent configuration across multiple use cases, avoiding the need for extensive hyperparameter tuning common in prior domain randomization studies. Given its encouraging performance across diverse scenarios, we believe that SAL can scale to broader industrial applications where training can be fully or mostly based on synthetic data. CC BY 4.0Correspondence Address: X. Zhu; KTH Royal Institute of Technology, Stockholm, Sweden; email: [email protected]; CODEN: JMSYECorrigendum in: Journal of Manufacturing Systems, 16 December 2025. https://doi.org/10.1016/j.jmsy.2025.12.012This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, Sweden. The computations were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre. We gratefully acknowledge colleagues at the Production Oskarshamn, Production Zwolle, Transmission Assembly, Engine Assembly, Academy, and Smart Factory Lab Departments at Scania CV AB for providing the CAD models and use cases. We also extend our thanks to Prof. Joakim Lindblad at the Department of Information Technology, Uppsala University, for his valuable insights and constructive feedback on this study.</p
Decision Making in Wood Supply Chain Operations Using Simulation-Based Many-Objective Optimization for Enhancing Delivery Performance and Robustness
No full textWood supply chains are complex, involving many stakeholders, intricate processes, and logistical challenges to ensure the timely and accurate delivery of wood products to customers. Weather-related variations in forest road accessibility further complicate operations. This paper explores the challenges faced by forest managers in targeting many delivery requirements—four or more. To address this, simulation-based optimization, using NSGA-III, a many-objective optimization algorithm, is proposed to simultaneously optimize often conflicting objectives primarily by minimizing delivery lead time, delivery deviations in backlogs, and delivery variation. NSGA-III enables the exploration of a diverse set of Pareto-optimal solutions that show trade-offs across a flexible set of four, or more, delivery objectives. A Discrete Event Simulation model is integrated to evaluate objectives in a complex wood supply chain. The implementation of NSGA-III within the framework allows forestry decision-makers to navigate between different harvest schedules and evaluate how they target a set of preference-based delivery objectives. The simulation can also provide detailed insights into how a specific harvest schedule affects the supply chain when post-processing possible solutions, facilitating decision making. This study shows that NSGA-III could substitute NSGA-II to optimize the wood supply chain for more than three objective functions.CC BY 4.0Correspondence: [email protected] or [email protected] (K.W.); [email protected] (A.H.C.N.)This research was supported by the Swedish Foundation for Strategic Research through the project FID17-0043.</p
Incorporating risk preferences in forecast selection
No full textThis paper introduces a methodology for incorporating risk preferences directly into forecasting model selection. The relative model information score, estimated from either a point-based information criterion or cross-validated errors, leverages the full distribution to map different risk propensities. We show that standard model selection in the literature is risk-agnostic. A risk-neutral stance is represented by the median of the relative model information score distribution, which characterises the plausibility of a model choice, while risk-averse and risk-tolerant choices correspond to its upper and lower quantiles. Our empirical evaluation demonstrates that risk-neutral and risk-averse selections consistently outperform the benchmark risk-agnostic choice in both point and quantile forecast accuracy. Moreover, we show that a risk-tolerant selection is beneficial during periods of extreme disruption. The proposed methodology provides a robust and flexible way to manage the forecast modelling risk, improving forecast accuracy and aligning forecasting modelling with stakeholders’ risk profiles.PRACTITIONER SUMMARY: In this research we introduce a methodology for incorporating stakeholders’ risk preferences directly in the forecasting process. Given a degree of risk-aversion, or risk-tolerance, our methodology can guide the selection of the appropriate forecast that exhibits these risk characteristics in its errors. Risk-averse forecasts will minimise the probability of large errors, while risk-neutral forecasts will focus on minimising errors irrespective of their magnitude. The standard model selection statistics in the literature are typically risk-agnostic and do not enable the analyst to implement a preference. Using data from retailing, we provide evidence that both risk-neutral and risk-averse forecasts outperform the standard solutions both in terms of point and quantile accuracy (from 4% to 82% depending on the case), with longer forecast horizons benefiting from increased risk-aversion. Additionally, we demonstrate that risk-tolerant forecasts are useful when there are large disruptions, resulting in reduced forecast errors. Our methodology is robust with respect to the choice of the exact level of risk-preference, therefore reducing the need to precisely elucidate the risk profiles of stakeholders, which can be challenging in practice. The proposed approach can be operationalised with any statistical, machine learning, or judgmental forecasts, allowing direct incorporation of user preferences in model selection. Moreover, it does not require any changes in the forecasting models, simplifying its adoption in practice. Our work constitutes a first step in incorporating stakeholder risk preferences into the forecasting process, and therefore into the decisions supported by these forecasts.CC BY 4.0Published online: 07 Feb 2026Taylor & Francis Group an informa businessCONTACT Nikolaos Kourentzes [email protected] funding was received for this research.</p
AdAPT : Advertisement detector adaptation under newspaper domain shift with null-based pseudo-labeling
No full textDetecting advertisements in digitized newspapers is a key step in large-scale media analytics and digital archiving. However, variations in layout, typography, and advertisement design across publishers and time periods cause significant domain shifts that reduce the generalization ability of supervised detectors. This paper presents AdAPT, a confidence-guided pseudo-labeling pipeline for unsupervised domain adaptation in advertisement detection. The proposed method leverages both advertisement-free (Null) and advertisement-containing pages from unlabeled target domains to generate reliable pseudo-labels. By retraining a YOLO-based detector using labeled source data combined with filtered pseudo-labeled target samples, AdAPT achieves robust adaptation without requiring manual annotation. Experiments conducted on two unseen newspapers (Adresseavisen and iTromsø) demonstrate that Null-based pseudo-labeling provides the most stable and accurate adaptation, yielding up to 38% error reduction compared to the baseline. The results highlight AdAPT as a simple, scalable, and annotation-efficient solution for maintaining high-performance advertisement detection across diverse newspaper collections.CC BY 4.0Corresponding author: Faeze Zakaryapour SayyadReceived 16 October 2025, Revised 21 November 2025, Accepted 30 November 2025, Available online 1 December 2025, Version of Record 3 December 2025.Declaration of competing interestThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Faeze Zakaryapour Sayyad reports financial support was provided by Mid Sweden University. Faeze Zakaryapour Sayyad reports a relationship with Knowledge Foundation (kks.se) within the Industrial graduate school Smart Industry Sweden, Media Research company that includes: employment and funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.AcknowledgmentThis work was supported in part by The Knowledge Foundation(kks.se) within the Industrial graduate school Smart Industry Sweden and Media Research AB. The authors would also like to thank Oscar Berg for his valuable comments on this work.</p
“Take Nothing on Its Look” : Revealing Users’ Expectations and Experiences in Social Human–Robot Interaction
No full textThe use of social robots in many sectors of society is predicted to progressively increase. Therefore, exploring how expectations play a role in and change users’ experiences when interacting with these robots over time is necessary. From an interpretative and insight-driven approach, our aim was to explore how humans experience in-person interactions with the social robot Pepper, which was equipped with the OpenAI GPT-3 language model. Qualitative data from 62 video recordings of the interactions with Pepper and post-test interviews were collected from 31 participants. An experiential reflexive thematic analysis was applied. The main findings include various levels of interaction quality, different interaction strategies, and elements influencing the users’ expectations and experiences, which were synthesized into a coherent framework. It appears that the participants adapted their interaction strategies based on their expectations and the perceived capability of the robot, which influenced their experiences. This reveals that positive user experience is not solely determined by interaction quality, showing the interplay among these aspects when interacting with a social robot. To conclude, our findings underscore the intricate nature of the role of user expectations and experiences in social human–robot interaction. The work adds complementary qualitative approaches to the Human–Robot Interaction community to provide additional insights on interacting with social robots.CC BY-NC-ND 4.0Authors’ Contact Information: Jessica Lindblom (corresponding author), Department of Information Technology, Uppsala University, Uppsala, Sweden; e-mail: [email protected]: 22 December 2025. Online AM: 17 October 2025. Accepted: 24 September 2025. Revised: 12 September 2025. Received: 12 May 2024This work was partially supported through the Knowledge Foundation, Sweden as part of the Recruitment and Strategic Knowledge Reinforcement Initiative. The publication of this article was supported by the Uppsala University Library, Sweden.</p
The ‘Good!’, the ‘Great!’ and the ‘Brilliant!’ : Exclamations and teacher artwork in volleyball teaching
No full textBackground: Physical education (PE) research has contributed to learning-based perspectives on the why, what and how of teaching PE. Within this context, ball games have been criticized for reflecting the traditional sport discourse, which is not always conducive to a PE curriculum that focuses on equity and movement capability. Furthermore, research highlights the complexity of the teacher’s role in the gym, where clear and simple communication should clarify learning objectives and support enhanced student engagement. This article seeks to reinterpret a frequently observed behaviour in the teaching of ball games, namely teachers’ common use of exclamations like ‘good!’, ‘great!’ or ‘brilliant!’ Purpose: This article aims to (1) describe and explain how teacher exclamations during a volleyball lesson are pertinent to the teacher's and students’ creation of purposeful contexts, and (2) identify how the use of exclamations can be conducive to students learning specific PE content and aesthetics during a volleyball lesson. This article adopts a transactional perspective of teaching, framing it as a creative action that shapes the learning environment. Method: The data consist of video recordings of volleyball lessons in Year 9. A video-ethnographic approach enables an in-depth analysis of teacher–student transactions throughout a full PE lesson. In addition to a fixed camera view of the gym, a wireless GoPro camera attached to the teacher provided a unique perspective, capturing the nuances of the teacher's verbal and non-verbal communication. Findings: The findings reveal that exclamations are not merely expressions of encouragement but integral to creating a cohesive and purposeful learning environment. Exclamations serve as confirmations of students’ actions and operate at individual, local and general levels to address diverse student needs. This helps students remain attuned to the flow of the lesson and contributes to the accumulation of meaning. Conclusions: By reinterpreting a frequently observed behaviour in PE teaching, namely teachers’ common use of exclamations, this analysis demonstrates that such exclamations can be important tools in the art of PE teaching and learning. Through voice, tone and timing, the teacher calls into existence an experience from multiple and durational educational transactions, guiding students towards ‘the good play’ rather than ‘the competitive play’.CC BY 4.0Received 03 Mar 2024, Accepted 17 Jan 2026, Published online: 29 Jan 2026CONTACT Joacim Andersson [email protected] Department of Sport Science, Malmö University, Jacob Bagges gata 2, 211 19 Malmö, SwedenThis work was supported by Vetenskapsrådet (Swedish Research Council): [Grant Number 2021-05261].</p
Are you cold? : A retrospective journal review of perioperative hypothermia
No full textBakgrund: En operation innefattar olika faser, den pre-, intra-, och postoperativa fasen, sammanfattningsvis kallas förloppet för perioperativ vård. Patienter riskerar att drabbas av hypotermi under det perioperativa förloppet till följd av olika faktorer. Perioperativ hypotermi definieras som en uppmätt kroppstemperatur under 36 grader Celsius. Perioperativ hypotermi kan leda till komplikationer som infektioner, försämrad sårläkning vilket leder till längre vårdtider och innebär ett ökat lidande för patienten. Syfte: Syftet var att undersöka förekomsten av perioperativ hypotermi hos slutenvårdade patienter som har genomgått en operation. Metod: En retrospektiv studie med kvantitativ metod har gjorts på 8428 journaler där temperaturmätning förekom. Vid aktuellt sjukhus dokumenteras kroppstemperaturen vid operationsstart, operationsslut och på den postoperativa avdelningen. Resultat: Resultatet visar att den perioperativa hypotermin har ökat vid det aktuella sjukhuset. Det är framför allt äldre patienter som har drabbats, framför allt kvinnor. En operationstid under 60 minuter förekom hypotermi på den postoperativa avdelningen och vid en operationstid över 60 minuter förekom hypotermi vid operationsslut och på den postoperativa avdelningen. Konklusion: Resultatet tydliggör förekomsten av hypotermi hos patienter i det perioperativa förloppet på det aktuella sjukhuset. Riskfaktorer som ålder, kön och operationstid i relation till varandra är viktig kunskap som kan appliceras i verksamheten och därmed kan yrkesverksamma sätta in åtgärder i tid för att hjälpa patienten bibehålla sin kroppstemperatur.Background: An operation includes different phases, the pre-, intra-, and postoperative phase, in short called perioperative care. Patients risk being afflicted with perioperative hypothermia due to various factors. Perioperative hypothermia is defined as a body temperature below 36 degrees celsius. Perioperative hypothermia can lead to complications such as infections, impaired healing of wounds which in turn can lead to increased suffering. Aim: To research the prevalence of perioperative hypothermia among inpatient care patients who underwent surgery. Method: A retrospective study with a quantitative method was performed on 8428 journals where a measurement of temperature was made. At the chosen hospital body temperatures are measured at the start and end of surgery, as well as during postoperative care. Findings: Results show that perioperative hypothermia has increased at the hospital in question. A majority of those affected were older women. An operation time of less than 60 minutes resulted in postop hypothermia, and with an operation time of more than 60 minutes, hypothermia occurred at the end of the operation and in the postoperative ward. Conclusion: The results clarify the prevalence of hypothermia in patients during the perioperative phases at the hospital in. Risk factors such as age, gender and operation time in relation to each other are important knowledge that can be applied in the practice and thus professionals can take timely measures to help the patient maintain their body temperature
Transfer line balancing problem : A comprehensive review, classification, and research avenues
No full textThe Transfer Line Balancing Problem (TLBP) is characterized as the challenge of optimally distributing tasks across various workstations in an automated machining line to ensure its maximum efficiency. This problem holds pivotal importance for industries reliant on high-volume production, such as the automotive and aerospace sectors, where it directly influences the overall productivity and cost efficiency of the manufacturing process. TLBP has been studied for over two decades, and many problem variants and solution approaches have been devised to address real-world challenges. Despite the long history of the topic, no review study exists to shed light on its past, current, and future developments. This study conducted a systematic literature review on TLBP to identify and address the research gaps, focusing on classifying existing studies. A tuple notation classification framework has been introduced to organize TLBP research based on system configuration, problem characteristics, and optimization objectives. This framework offers a structured overview of the field, clarifying the current state of research and highlighting prospective research pathways. Consequently, this review study establishes itself as a foundational guide for academics and industry professionals interested in TLBP studies.CC BY 4.0Available online 29 January 2025, 110913Corresponding author: [email protected]; [email protected] first three authors would like to acknowledge funding from the Knowledge Foundation (KKS) and Sweden’s Innovation Agency through the ACCURATE 4.0 (grant agreement No. 20200181) and PREFER projects, respectively.ACCURATE 4.0PREFE