220861 research outputs found
Sort by
Improving Question Embeddings With Cognitive Representation Optimization for Knowledge Tracing
No full textDate of Publication: 08 October 2025The knowledge tracing (KT) aims to track changes in students’ knowledge status and predict their future answers based on their historical answer records. Current research on KT modeling focuses on predicting student’ future performance based on existing, unupdated records of student learning interactions. However, these approaches ignore the distractors (such as slipping and guessing) in the answering process and overlook that static cognitive representations are temporary and limited. Most of them assume that there are no distractors in the answering process and that the record representations fully represent the students’ level of understanding and proficiency in knowledge. In this case, it may lead to many lack of synergy and incoordination issue in the original records. Therefore we propose a cognitive representation optimization for KT (CRO-KT) model, which utilizes a dynamic programming algorithm to optimize structure of cognitive representations. This ensures that the structure matches the students’ cognitive patterns in terms of the difficulty of the exercises. Furthermore, we use the co-optimization algorithm to optimize the cognitive representations of the subtarget exercises in terms of the overall situation of exercises responses by considering all the exercises with co-relationships as a single goal. Meanwhile, the CRO-KT model fuses the learned relational embeddings from the bipartite graph with the optimized record representations in a weighted manner, enhancing the expression of students’ cognition. Finally, experiments are conducted on three publicly available datasets respectively to validate the effectiveness of the proposed cognitive representation optimization model. The source code of CRDP-KT is available at https://github.com/bigdata-graph/CRO-KT.Lixiang Xu, Xianwei Ding, Xin Yuan, Zhanlong Wang, Lu Bai, Enhong Chen, Philip S. Yu, Yuanyan Tan
Evaluating geometric primitives for crater-based pose estimation
No full textAvailable online 16 April 2025Crater-based pose estimation (CBPE) is an integral component of crater-based navigation (CBN) pipelines for cislunar, asteroid and planetary missions. CBPE aims to estimate the position and attitude of a spacecraft using observed craters and their correspondences in a catalogue of known craters. There are typically two paradigms in CBPE: point-based and ellipse-based methods. To the best of our knowledge, there has never been a comprehensive evaluation of these two geometric primitives for CBPE. While a recent study suggests ellipse-based methods are superior, we argue that the study is inconclusive due to the research gaps in pointbased methods. Existing point-based methods make one of two strong assumptions, i.e., the camera has to be nadir-pointing, or, the observed terrain has to be smooth. However, these assumptions do not hold in general CBN scenarios. To this end, we propose a new point-based method, dual perspective-n-point (PnP), that generalises to these scenarios. We demonstrate dual PnP’s strengths over existing point-based methods in two newly collected datasets. Furthermore, our findings provide strong evidence indicating the superiority of using ellipse-based geometric primitives over point-based geometric primitives for CBPE. This paper evaluates the two geometric primitives for CBPE, contributing to one of the most comprehensive analyses of CBPE methods to date.Sofia McLeod, Chee-Kheng Chng, Tat-Jun Chi
Educating radiography students via simulation-based learning in preparation for clinical placement work integrated learning (WIL): A scoping review of student perspectives
No full textIntroduction: The objectives of this scoping review are to question, which simulation activity do students’ believe best prepares them for their clinical rotations in general (projection) radiography? Virtual reality (VR) or traditional (high and low fidelity) simulation?, and what are the knowledge gaps in this area?
Methods: The search protocol was created and conducted according to the Population, Concept, Context (PCC) framework for scoping reviews across PubMed, CINAHL, ERIC, and Scopus databases. Undergraduate radiography students were the population of interest, the concept was simulation-based learning, and the context was preparedness for clinical placement.
Results: A total of 18 studies were included in this scoping review. The benefits of simulation as an instructional method that were identified included students being able to learn, make mistakes, and practise in a safe environment. Several additional themes emerged including confidence with both clinical and technical skills, preparedness for practice, theoretical understanding, visual learning, skill repetition, and reinforcement.
Conclusion: Simulation-based learning was shown to have a profound impact on the preparedness of undergraduate radiography students for clinical placement. Placements provide students with the opportunity to apply theoretical concepts to practice-based tasks and enable authentic learning situations to ensure graduates are work-ready at the conclusion of their studies.
Implications for practice: Synthesis and evaluation of published literature indicates simulation-based learning enables educators to implement best practice to prepare students for the clinical environment. This translates into students reporting being more confident with knowledge recall, performing under pressure, a reduction in student errors, and ultimately higher quality patient care and improved patient outcomes
Extracellular polymeric substances bestowed effective interfacial electron transfer process during biohybrid-activated periodate: Dual enhancement of hydrophilicity and electron conductivity
No full textData source: supplementary material, https://doi.org/10.1016/j.apcatb.2025.125797While extracellular polymeric substances (EPS) are crucial for optimizing the functionality of microbes-derived biohybrids during catalysis, the impact of EPS on the catalytic behavior of biohybrids (catalytic activity and mechanism) needs further elucidation. Herein, the intrinsic connection between EPS and the catalytic behavior of biohybrids was comprehensively investigated using Shewanella Oneidensis MR-1-derived reduced graphene oxide-anchored nanoscale iron sulfide (SO-rGO@FeS) and its constituent components (SO-rGO and SO-FeS) as models. All biohybrids/periodate (PI) systems studied exclusively produced the high oxidation potential metastable complex (PI*) for degrading sulfamethoxazole (SMX) via a nearly 100 % interfacial electron-transfer process. Specifically, EPS (e.g., proteins, polysaccharides, and c-type cytochromes) improved the hydrophilicity and electron conductivity of all biohybrids, which enhanced the affinity and electron migration between biohybrids, PI, and SMX, in turn accelerating the interfacial electron-transfer process. Overall, this study provides a novel adaptive paradigm for expanding the potential application of biohybrids in the field of advanced oxidation
Understanding the interplay among parental involvement, parental self-regulation, and child adjustment: A latent profiles analysis and cross-validation
No full textHigh levels of parental involvement in children's learning and education bring considerable benefits to children and act as a protective factor for difficulties in children's social, emotional, and behavioral adjustment. Parental self-regulation and efficacy have been found to have positive associations with both their contributions to the home-school partnership and to children's wellbeing. However, most previous studies examining these relationships have applied an “average” approach that overlooked potential individual differences. Using latent profile analysis, this study aimed to investigate the individual differences in the interplay among parental involvement, parental self-regulation and parenting self-efficacy, and children's social, emotional, and behavioral problems. Data were drawn from a survey of 2265 parents of primary-school-aged children in Australia. The sample was randomly split into two similar sized subsamples (N = 1147 and N = 1125) to cross-validate the profile solution and the results of subsequent analyses. We identified four distinct parent profiles: proactive (42.4%), adequate (28.6%), help-seeking (16.9%), and disengaged (12.0%). Profile memberships were associated with a range of child and parent demographic factors, parenting practices, family adjustment, and parental emotional adjustment. These findings contribute to a better understanding of the considerable individual differences in the parent population. These findings also highlight the need for schools to utilize practical strategies to promote parenting capacity, strengthen home-school partnerships, and address child adjustment difficulties.Tianyi Ma, Cassandra L. Tellegen, Julie Hodges, Christopher Boyle, Matthew R. Sander
Multi-view debiasing representation learning for recommender systems
No full textRecommender systems aim to predict user feedback on unseen items, but confounding bias, particularly from latent confounders, presents a major challenge. Existing debiasing methods in recommender systems often overlook the complex interplay among multiple features and the subtleties of user preferences. To address this, we propose a novel framework called Multi-View-based Identifiable Debiased Learning (MViDL) for recommendations, even in the presence of latent confounders. Specifically, MViDL first employs a multi-view framework to discern interactions between user and item features, unearth user interests in specific items, and capture fundamental user and item ID information. To mitigate the effects of latent confounders, MViDL incorporates the identifiable Variational Auto-Encoder (iVAE) to efficiently infer the latent representation from a set of proxy variables and adjusts for the learned latent representation to mitigate confounding bias. We further provide a theoretical analysis of the identifiability of the latent representations. Extensive evaluations on three real-world datasets highlight the superiority of MViDL. Specifically, our approach achieves average improvements of approximately 6.03% and 5.70% in NDCG@K and Recall@K over the state-of-the-art (SOTA) baselines on Coat, 3.54% and 2.29% on Yahoo!R3, and 1.49% and 2.47% on KuaiRand
Daily, prospective associations of sleep, physical activity, and sedentary behaviour with affect: a Bayesian multilevel compositional data analysis
No full textData source: supplementary data, https://doi.org/10.1016/j.psychsport.2025.102997Background: 24 h behaviours (sleep, time awake in bed, moderate-to-vigorous physical activity [MVPA], light physical activity [LPA], and sedentary behaviour [SB]) may influence long-term mental health through their associations with affective experiences in everyday life. Here, we investigated the daily, prospective associations between 24 h behaviours and affect.
Methods: Actigraphy-measured 24 h behaviours and self-reported affect data were collected across 7–15 consecutive days in healthy, community-dwelling adults (N = 354, Mage = 22.61 y, 73 % female) providing 2872 days of data. Bayesian multilevel compositional data analysis evaluated how reallocating time between behaviours was associated with next-day affect at between- and within-person levels.
Results: Associations between 24 h behaviours and next-day affect emerged at the within-person, not between-person level. Relative to the remaining behaviours, more LPA predicted 0.14 [95 % CI 0.03, 0.26] higher high arousal positive affect, whereas less SB predicted lower high and low arousal positive affect (−0.14 [-0.25, −0.02] and −0.12 [-0.24, −0.01], respectively) higher high arousal negative affect (0.13 [0.03, 0.23]). Further, within-person 30-min reallocation to LPA from SB, sleep, and time awake in bed also predicted ≥0.03 [0.00, 0.06] higher high arousal positive affect. 30-minute reallocation of time to LPA and MVPA from SB predicted 0.04 [0.01, 0.06] higher high arousal positive affect and −0.02 [-0.04, −0.00] lower low arousal negative affect.
Conclusion: Findings provide stepping stone evidence for identifying optimal daily compositions of 24 h behaviours for affective enhancements in healthy individuals. Replacing time in SB with LPA and MVPA for improving affect should be experimentally tested in daily settings and clinical populations, to inform diagnostic and intervention strategies for better daily affect and mental health
A digital twin-based framework for predictive quality assurance and supply chain resilience in the automotive industry
No full textThe automotive industry faces growing challenges in ensuring supply chain resilience (SCR) and predictive quality assurance (PQA), particularly amid global disruptions. Traditional quality systems often lack the traceability and adaptability needed in this dynamic environment. Addressing this gap, this study proposes a novel digital twin-enabled framework based on a structured seven-phase, five-stage methodology, termed the 7D model. Aligned with international automotive task force (IATF) standards, the framework leverages real-time IoT data and historical metrics to simulate disruptions, monitor key performance indicators (KPIs), and enable data-driven, proactive quality interventions.
A case study from a tractor manufacturer illustrates the framework’s applicability in an emerging market context. Despite operating with limited digital infrastructure, the company’s engagement with lean practices demonstrates the feasibility and scalability of the 7D-PQA model. Comparative analysis against conventional problem-solving methods validates the framework’s enhanced capacity for resilience, traceability, and predictive quality. This work advances the field by offering the first IATF-aligned DT framework for PQA in the automotive sector, with broader implications for digital transformation across manufacturing industries
Accelerometer-measured physical activity and sleep of adolescents in Ho Chi Minh City, Vietnam: a school-based cross-sectional study
No full textThis study was to measure physical activity (PA) and sleep habits among junior high school students in HCMC and identify factors associated with World Health Organization (WHO) recommendations. We performed a cross-sectional study with 1,023 junior high school students were randomly selected. Participants wore wrist-worn accelerometers for seven consecutive days to objectively record PA and sleep using validated methods. Multivariate models were used to identify predictors of achieving WHO recommendations for PA (>= 60 min/day moderate-to-vigorous physical activity-MVPA) and sleep (8-10 hours/night). Data completed on 948 students with 47.5% males and the mean age was 12.9 years. Only 35.1% and 42.8% met WHO PA and sleep recommendations, respectively. Boys engaged in significantly more MVPA than girls, while girls spent more time in light activity. Girls also showed better sleep quality-longer sleep duration, higher efficiency, and shorter latency-whereas older adolescents had shorter and less efficient sleep than younger peers. Across all groups, average sleep duration was below WHO recommendations. After adjusting for other factors, boys were significantly more likely than girls to achieve the PA guidelines. Overall, the findings indicate that gender, age, BMI, parental modeling, family encouragement, and supportive school environments are the strongest predictors of whether adolescents meet recommended PA levels. Girls, younger age, overweight/obesity, family sleeping reminders and quiet sleep environment were significant factors of adhering sleep recommendation. Our study revealed the multifactorial interaction of individual, family, and environmental factors in meeting WHO recommended levels for PA and sleep in adolescents. Multidisciplinary interventions are required to promote adolescent behaviors
Protein binding assessment of immobilized nanobody using interferometric nanoporous platform
No full textProtein sensors are key tools for infectious disease diagnosis and monitoring. The medical diagnostics field is now rapidly identifying key protein markers for the detection of a range of conditions, from early cancer development through to assessment of dementia risk a decade prior to symptoms onset. Many point-of-use diagnostic tools form part of standard protocols for small molecule sensing in healthcare, including glucose and ketone body sensors. Label-free optical biosensors have emerged as reliable detection tools that provide high versatility and adaptability to detect a broad range of target analytes. Herein, we combined nanoporous anodic alumina (NAA) photonic chip technology with tailor-engineered “nanobodies”—recombinant variable domains of heavy-chain-only antibodies—to achieve high affinity binding to human serum albumin (HSA). We monitored dynamic shifts in the optical fingerprint of nanobody-conjugated NAA platforms in real time when these were exposed to target proteins, through reflectometric interference spectroscopy (RIfS). We performed a comprehensive characterization of the sensing performance, where binding mechanisms were elucidated through kinetic profiles. The nanobody-conjugated NAA protein sensor demonstrated good affinity, selectivity, and sensitivity toward HSA. Our analysis revealed a sensitivity of 27.3 ± 3.2 nm µM⁻¹ and low limit of detection of 16.3 ± 1.6 µM, which are well-below the ranges for diagnosis of medical conditions such as nephrotic syndrome. The obtained results revealed that the RIfS-based protein sensor integrated with nanobody-modified NAA has promising potential to develop point-of-care platforms for clinical diagnosis of diseases.Cheryl Suwen Law, Jayden Revink, Joel Lee, Juan Wang, Andrew D. Abell, Fiona Whelan, Abel Santo