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FPGA Innovation Research in the Netherlands:Present Landscape and Future Outlook
No full textFPGAs have transformed digital design by enabling versatile and customizable solutions that balance performance and power efficiency, yielding them essential for today's diverse computing challenges. Research in the Netherlands, both in academia and industry, plays a major role in developing new innovative FPGA solutions. This survey presents the current landscape of FPGA innovation research in the Netherlands by delving into ongoing projects, advancements, and breakthroughs in the field. Focusing on recent research outcome (within the past 5 years), we have identified five key research areas: a) FPGA architecture, b) FPGA robustness, c) data center infrastructure and high-performance computing, d) programming models and tools, and e) applications. This survey provides in-depth insights beyond a mere snapshot of the current innovation research landscape by highlighting future research directions within each key area; these insights can serve as a foundational resource to inform potential national-level investments in FPGA technology
Interdisciplinary Research Project 'AI Shield'
No full textIn the modern day and age, cybersecurity faces numerous challenges. Computer systems and networks become more and more sophisticated and interconnected, and the attack surface constantly increases. In addition, cyber-attacks keep growing in complexity and scale. In order to address these challenges, security professionals started to employ generative AI (GenAI) to quickly respond to attacks. However, this introduces challenges in terms of how GenAI can be adapted to the security environment and where the legal and ethical responsibilities lie. The Universities of Twente and Groningen and the Hanze University of Applied Sciences have initiated an interdisciplinary research project to investigate the legal and technical aspects of these LLMs in the cybersecurity domain and develop an advanced AI-powered tool. This project is currently being developed and will form the basis of the grant application to be submitted in the near future at the Dutch Research Council ('Nederlandse Organisatie voor Wetenschappelijk Onderzoek' or NWO).</p
Ordinal Patterns Based Change Points Detection
No full textThe ordinal patterns of a fixed number of consecutive values in a time series is the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series. In this work, we prove the asymptotic normality of the relative frequency of ordinal patterns for time series with linear increments. Moreover, we apply ordinal patterns to detect changes in the distribution of a time series
How Do Motivation and Self-Regulation Shape Learners’ Satisfaction with E-Learning?
Get PDFLearner satisfaction is a crucial indicator of the overall quality of the e-learning experience. While numerous studies have focused on identifying the “what”—the factors that predict satisfaction—there has been limited investigation into the “how,” or the mechanisms linking these factors to satisfaction. This study addresses this gap by examining the role of two key individual factors: motivation and self-regulation. It posits that while these factors are directly related to learner satisfaction, they also have an indirect effect through their connection with the Community of Inquiry (CoI) presences, namely, social, cognitive, and teaching presences. Data were collected from 247 master's students enrolled in online programs at three state universities in Iran, and path analysis was conducted to explore the interactions between these variables. The findings provide valuable insights into the complex relationships at play, revealing that self-regulation is a more significant predictor of learner satisfaction than motivation. Moreover, while both factors were directly associated with satisfaction, their effects were also mediated by the three CoI presences. Among these, cognitive presence emerged as the most influential mediator, emphasizing its pivotal role in enhancing the e-learning experience. The findings of this study offer new insights into designing effective e-learning environments that not only engage learners but also effectively support their satisfaction.<br/
Anchorlogy:An Ontology for Anchoring Bias Detection in Forecasting
Get PDFAnchoring bias is one of the most prevalent biases within forecasting. It distorts managers’ estimations whenever context-driven intervention to the statistical model output is required. Consequences extend beyond a single organization since forecasting affects order quantity decisions and, therefore, the relations among suppliers, potentially generating a bullwhip effect throughout the supply chain. Anchoring bias can have a significant impact, and despite being related to a numerical value, its detection is very complex. Moreover, it tends to be recurrent when the context that caused the distortion is not explored and precisely understood. Current detection approaches are incomplete, as they do not make explicit the directional component of anchors or their meaning to the decision maker’s mental heuristics. In this work, we present Anchorlogy, an ontology devised to explicitly provide the required context to detect and mitigate anchoring bias during a decision-making process, and a metrological approach to measure it while addressing the deficiencies found in other metrics in the current psychological literature. Our proposal was validated by applying it to two case studies in the forecasting domain, and the results show that it effectively prevents the bullwhip effect in real-world scenarios.</p
Hierarchical Transformer Fusion of Gaze Attention and Muscle Activity for Forearm Movement Estimation
Get PDFTracking forearm movement via measured physiological signals is crucial for understanding human motor control mechanism. Current methods mainly use muscle-derived signals to predict arm movements while often overlooking the potential role of gaze attention, which is important for hand-eye coordination and instant and continuous motion planning and execution. In this study, we explored the impact of gaze on motion tracking. A hierarchical transformer-based structure was developed to integrate gaze into muscle activity signals for recovering the joint trajectory. To collect the dataset, six subjects were recruited to perform arm motions broadly involved in daily activities; the measured signals from the muscle activity and gaze attention were used to train and evaluate the proposed method. A performance comparison was conducted between the models using solely muscle activity signals and both muscle and gaze information. The experimental results showed the important role of gaze information involved in motion prediction and the motor control mechanism. This research also gained insights on how to integrate gaze information into the muscle signals, which offers an alternative to bringing artificial intelligence to be engaged in the framework of motion tracking. Consequently, it is important for future designs of biomechanical sensors and wearable robotics systems.</p
A spatiotemporal framework to assess the bio-geomorphic interplay of saltmarsh vegetation and tidal emergence (Western Scheldt estuary)
Get PDFSea level changes will significantly drive hydrodynamic, morphological, and ecological development of estuaries. However, the interplay of geomorphology and vegetation at estuary scales remains unclear. To better understand this process, we take the Western Scheldt estuary in the Netherlands as an example to reveal the link between changes in emersion duration and vegetation dynamics in the period 1993–2016. We found that tidal flats in the Western Scheldt become steeper—higher intertidal areas increased in elevation and emersion duration, whereas the low-lying edges of tidal flats experienced a decrease in elevation and emersion duration. We found that longer emersion duration was associated with increased plant diversity and cover. Furthermore, we detected the unique spatiotemporal response patterns of four abundant plant species to geomorphological variations. Our study suggests that on a large estuary scale, geomorphological changes are coupled to the richness and cover of plant communities, and that potential changes in relative sea level can induce structural modifications of the plant communities. It also emphasizes the importance of assessing the potential effects of localized relative sea level changes while considering all aspects of natural processes and direct and indirect human influences. Our study provides a framework to assess the bio-geomorphic processes in a spatially explicit way.</p
Automatic prostate volume estimation in transabdominal ultrasound images
Get PDFIntroduction: Prostate cancer is a major health concern requiring accurate and accessible methods for early detection and risk stratification. Prostate volume (PV) is a key parameter in multivariate risk assessment, traditionally measured using transrectal ultrasound (TRUS). While TRUS provides precise measurements, its invasive nature affects patient comfort. Transabdominal ultrasound (TAUS) offers a non-invasive alternative but is limited by lower image quality and operator dependence. This study presents a deep-learning-based framework for automatic PV estimation using TAUS, aiming to improve non-invasive prostate cancer risk stratification. Methods: A dataset of TAUS videos from 100 patients (median age 67, 95-percentile range 55–81.2) was curated, with expert-delineated prostate boundaries and diameter calculations as ground truth. The framework integrates deep-learning models for prostate segmentation in both axial and sagittal planes, automatic diameter estimation, and PV calculation. Segmentation performance was evaluated using Dice correlation coefficient (%) and Hausdorff distance (mm), while volume estimation accuracy was assessed through volumetric error (mL). Results: The axial model outperformed the sagittal model, achieving a Dice score of 0.76 ± 0.16 versus 0.68 ± 0.21, a Dice-MidPlane of 0.91 ± 0.06 versus 0.83. ± 0.10, and a Hausdorff distance of 6.21 ± 4.33 mm versus 7.93 ± 4.27 mm. The framework estimated PV with a mean volumetric error of −2.1 mL (95 % limits of agreement: −16.9 to 21.1 mL), resulting in a relative error smaller than 25 %. Conclusion: These findings highlight the potential of deep learning for accurate, non-invasive PV estimation, supporting improved prostate cancer risk assessment.</p
Taking aim at research on esports teams:A systematic literature review and cross-disciplinary future agenda
Get PDFPurpose: This paper aims to systematically review and integrate the fast-growing literature on esports teams. Esports have evolved into hyper-competitive and professionalized settings with particular challenges, which need to be understood to develop and support sustainable esports teams. Likewise, esports teams share similarities with teams from professional sports and beyond, thus having the potential to inform team research in general.Design/methodology/approach: The authors leveraged a systematic literature review approach and conducted a structured keyword search in Web of Science. The results were extended by a journal-driven search and forward-backward citation tracking, resulting in a final sample of 92 articles, which were analyzed via qualitative content analysis.Findings: First, the authors find that research predominantly leverages quantitative study designs and samples of nonprofessional MOBA players. Second, four main themes that shape effectiveness in esports teams emerged: team compositional and structural features, leadership and external resources, team emergent states and team action processes. Third, the authors discuss blind spots within the literature that need more attention (e.g. psychological safety and stress management mechanisms) and how scholars can leverage the rich, multifaceted and high-resolution data existing in this context (e.g. game logs, audio and video recordings) to generate important insights on team dynamics valuable far beyond the esports domain. Finally, the authors discuss practical implications for players and teams to build and maintain sustainable esports teams.Originality/value: To the best of the authors’ knowledge, the authors provide the first systematic review on esports team effectiveness based on the Input-Mediator-Output-Input model and a critical evaluation of how it can fertilize esports research and practitioners.</p
Mobility injustice and agency:confronting border asymmetries in cross-border commuting under COVID-19 re-bordering policies
Get PDFThe immobility crisis resulting from the Covid-19 pandemic highlighted the unequal impact of national re-bordering policies on Eastern European border regions, emphasizing their marginalization due to uneven economic development and institutional fragility. This paper aims at understanding the dual vulnerabilities experienced by cross-border commuters in Europe, focusing on the structural inequalities and border asymmetries between core and peripheral regions. The Covid-19 pandemic serves as a case study to examine how existing structural inequalities and institutional vulnerabilities between core and peripheral regions manifest in European border areas. Based on semi-structured expert and commuter interviews from various European border regions—particularly those between Germany and Luxembourg, Denmark, the Netherlands, and Poland—the research demonstrates how the intersection of border asymmetries and institutional weaknesses deepened mobility injustices. Thematic analysis of the interviews shows that both structural factors and sudden policy changes, such as covidfencing and re- and de-bordering practices, worsened the precarious conditions faced by commuters, particularly in the Polish-German border region. The findings show that individual and territorial responses to rapid policy changes during the pandemic have also led to the emergence of new risks.</p