Prior Knowledge Research Articles

Using Topic Specific Pedagogical Content Knowledge to Study Indigenous Knowledge Integration in Grade 11 Physical Science Lessons: Teachers’ Perspectives

Many learners seem to struggle to grasp the concepts of forces and Newton’s law of motion, possibly owing to decontextualised teaching pedagogies used in science classrooms. This study investigates Grade 11 Physical Sciences teachers’ perspectives in South Africa on integrating Indigenous Knowledge (IK) to improve learners’ understanding of science, particularly in relation to forces and Newton’s laws of motion. It adopts a qualitative case study approach, grounded in interpretive and Indigenous research paradigms, and framed by the Ubuntu perspective, which emphasises community and collective well-being. Purposive sampling was used to select three voluntary participants, and data were generated through semi-structured interviews. Data analysis was based on Mavhunga and Rollnick’s Topic Specific Pedagogical Content Knowledge framework, focusing on learners’ prior knowledge, curricular saliency, difficulties in understanding, and teaching strategies. Key themes included learners’ prior knowledge, content prioritisation, challenges in integrating IK and conceptual teaching strategies. The study found that a novice teacher instinctively integrated IK without formally understanding it, aligning with effective teaching strategies. On the other hand, experienced teachers demonstrated more understanding of IK, recognising its potential to help learners move from familiar to new concepts. However, one of these experienced teachers viewed IK as somewhat outdated, despite its presence in their teaching practices. This mixed perspective underscores the need for further experimentation with IK in Physical Sciences lessons to better assess its impact on learners’ understanding, as teachers’ perspectives on its relevance and application vary.

An unsupervised approach for projection binning to reduce motion artifacts in free-breathing animal models.

Dynamic imaging holds great potential in the diagnosis and comprehensive evaluation of different diseases by capturing mechanical and dynamic characteristics of moving organs. Nonetheless, motion artifacts notably impair image quality, hindering accurate and localized analysis-particularly in free-breathing scenarios. In preclinical studies, traditional methods often necessitate artificial breathing control or use invasive techniques that do not permit functional lung assessment under normal physiological conditions, potentially biasing results and restricting longitudinal studies. This study aimed to mitigate motion artifacts and preserve temporal information, thus enhancing the spatiotemporal resolution of dynamic micro-CT images in free-breathing animals. We sought to combine the benefits of standard amplitude and phase binning within an unsupervised learning approach, without the need for iterative methods, prior knowledge, or alteration of the reconstruction process. Our approach facilitates accurate imaging of free-breathing animals under various protocols, without requiring artificial breathing control or invasive interventions, through a straightforward, immediately applicable retrospective analysis method. A novel periodic line-constrained K-means clustering technique was developed as an unsupervised method for projection/interleave binning. To validate this technique on preclinical micro-CT images, a syringe-spring system was engineered to simulate respiratory motion. Imaging was performed on this moving phantom with various breathing rates and inhale-to-exhale (I/E) ratios, as well as in free-breathing rats and rabbits. Additionally, we detail a method for extracting the breathing signal directly from the x-ray projection images and introduce a systematic approach for data imputation in limited-angle scenarios. We also established a metric for quantifying motion artifacts in our 4DCT images. The clustering method effectively integrated the benefits of both amplitude and phase binning, leading to a marked reduction in motion artifacts across all tests. Notably, our method yielded enhanced image clarity and improved accuracy in capturing dynamic lung volumes, evidenced by sharper diaphragm edges, better visibility of blood vessels, and diminished blurring and motion artifacts. Quantitative analysis using linear regression of diaphragm speed versus blur measure showed a near-zero slope for both rats and rabbits, indicating a substantial decrease in motion artifact presence compared to traditional binning methods. The periodic line-constrained K-means clustering method provides a robust solution for enhancing the quality of dynamic micro-CT imaging in preclinical studies. By reducing motion artifacts and improving image resolution, this approach enables more precise evaluations of lung function under physiologically relevant conditions. Future work will explore the application of this method to various respiratory disease models and assess its potential for broader clinical use in dynamic imaging of other organs.

A robust active noise control algorithm using an adjustable arctangent function

Abstract The filtered-x least mean square (FxLMS) algorithm is a classic algorithm in the field of active noise control (ANC). However, its stability is compromised in the presence of impulsive noise, resulting in a poor noise reduction outcome. To address this issue, a normalized step size adjustable filtered-x arctangent least mean square (NAFxatanLMS) algorithm is proposed in this paper, which operates without the need of prior knowledge for threshold estimation. The algorithm employs an adjustable arctangent function as the cost function to perform a nonlinear transformation of the error signal. A compression factor is incorporated into the cost function to control the compression degree of the error signal, and an automated adjustment strategy is proposed to balance convergence speed and robustness of the algorithm. Finally, an improved variable step size strategy is introduced to further enhance the algorithm's performance. Simulation results demonstrate that, compared to other algorithms, the proposed NAFxatanLMS algorithm exhibits superior convergence speed and noise reduction ratio.

Bayesian updating of the residual structural performance of existing concrete structures based on experimental tests

ABSTRACT Reliability analysis of existing concrete structures requires performance estimations which are affected by uncertainties related to system geometry, material characteristics, loading scenarios, and exposure conditions. A framework for Bayesian model updating with structural reliability methods of concrete structures is presented and validated based on the results of an experimental campaign on prestressed concrete (PC) bridge deck beams extracted from a dismantled 50-year-old viaduct. Subset simulation techniques are applied for structural reliability analysis. The prior knowledge of concrete mechanical properties and residual prestressing levels is updated based on the results of laboratory tests. The statistical residual performance of the PC deck beams is compared with the experimental results of a full-scale load test up to collapse. The structural modelling is developed with bi-dimensional finite elements for plane-stress analysis formulated in accordance with the Modified Compression Field Theory (MCFT). The comparison between experimental and numerical results allows to validate the modelling strategies, the outcomes of the experimental tests, and the Bayesian updating framework.

Had Enough of Experts? Quantitative Knowledge Retrieval From Large Language Models

ABSTRACTLarge language models (LLMs) have been extensively studied for their ability to generate convincing natural language sequences; however, their utility for quantitative information retrieval is less well understood. Here, we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid two data analysis tasks: elicitation of prior distributions for Bayesian models and imputation of missing data. We introduce a framework that leverages LLMs to enhance Bayesian workflows by eliciting expert‐like prior knowledge and imputing missing data. Tested on diverse datasets, this approach can improve predictive accuracy and reduce data requirements, offering significant potential in healthcare, environmental science and engineering applications. We discuss the implications and challenges of treating LLMs as ‘experts’.

Proactive distractor suppression in early visual cortex.

Avoiding distraction by salient yet irrelevant stimuli is critical when accomplishing daily tasks. One possible mechanism to accomplish this is by suppressing stimuli that may be distracting such that they no longer compete for attention. While the behavioral benefits of distractor suppression are well established, its neural underpinnings are not yet fully understood. In a functional MRI (fMRI) study, we examined whether and how sensory responses in early visual areas show signs of distractor suppression after incidental learning of spatial statistical regularities. Participants were exposed to an additional singleton task where, unbeknownst to them, one location more frequently contained a salient distractor. We analyzed whether visual responses in terms of fMRI BOLD were modulated by this distractor predictability. Our findings indicate that implicit spatial priors shape sensory processing even at the earliest stages of cortical visual processing, evident in early visual cortex as a suppression of stimuli at locations which frequently contained distracting information. Notably, while this suppression was spatially (receptive field) specific, it did extend to nearby neutral locations and occurred regardless of whether distractors, nontarget items, or targets were presented at this location, suggesting that suppression arises before stimulus identification. Crucially, we observed similar spatially specific neural suppression even if search was only anticipated, but no search display was presented. Our results highlight proactive modulations in early visual cortex, where potential distractions are suppressed preemptively, before stimulus onset, based on learned expectations. Combined, our study underscores how the brain leverages implicitly learned prior knowledge to optimize sensory processing and attention allocation.

Combining Prior Knowledge and Reinforcement Learning for Parallel Telescopic-Legged Bipedal Robot Walking

The parallel dual-slider telescopic leg bipedal robot (L04) is characterized by its simple structure and low leg rotational inertia, which contribute to its walking efficiency. However, end-to-end methods often overlook the robot’s physical structure, leading to difficulties in maintaining the parallel alignment of the dual sliders, which in turn compromises walking stability. One potential solution to this issue involves utilizing imitation learning to replicate human motion data. However, the dual telescopic leg structure of the L04 robot makes it difficult to perform motion retargeting of human motion data. To enable L04 walking, we design a method that integrates prior feedforward with reinforcement learning (PFRL), specifically tailored for the parallel dual-slider structure. We utilize prior knowledge as a feedforward action to compensate for system nonlinearities; meanwhile, the feedback action generated by the policy network adaptively regulates dynamic balance and, combined with the feedforward action, jointly controls the robot’s walking. PFRL enforces constraints within the motion space to mitigate the chaotic behavior of the parallel dual sliders. Experimental results show that our method successfully achieves sim2real transfer on a real bipedal robot without the need for domain randomization techniques or intricate reward functions. L04 achieves omnidirectional walking with minimal energy consumption and exhibits robustness against external disturbances.

Adaptive Weight Selection for Time-To-Event Data Under Non-Proportional Hazards.

When planning a clinical trial for a time-to-event endpoint, we require an estimated effect size and need to consider the type of effect. Usually, an effect of proportional hazards is assumed with the hazard ratio as the corresponding effect measure. Thus, the standard procedure for survival data is generally based on a single-stage log-rank test. Knowing that the assumption of proportional hazards is often violated and sufficient knowledge to derive reasonable effect sizes is usually unavailable, such an approach is relatively rigid. We introduce a more flexible procedure by combining two methods designed to be more robust in case we have little to no prior knowledge. First, we employ a more flexible adaptive multi-stage design instead of a single-stage design. Second, we apply combination-type tests in the first stage of our suggested procedure to benefit from their robustness under uncertainty about the deviation pattern. We can then use the data collected during this period to choose a more specific single-weighted log-rank test for the subsequent stages. In this step, we employ Royston-Parmar spline models to extrapolate the survival curves to make a reasonable decision. Based on a real-world data example, we show that our approach can save a trial that would otherwise end with an inconclusive result. Additionally, our simulation studies demonstrate a sufficient power performance while maintaining more flexibility.

False memories from nowhere: Humans falsely recognize words that are not attested in their vocabulary.

Semantic knowledge plays an active role in many well-known false memory phenomena, including those emerging from the Deese-Roediger-McDermott (DRM) task. Indeed, in this experimental paradigm, humans tend to falsely recognize newly presented words via activation of other previously shown stimuli. In the present study we aimed to test what happens in cases in which no apparent prior semantic knowledge is available, like in the case of entirely novel lexical stimuli. To do so, we evaluated semantic similarity effects in a DRM task with lists entirely composed by pseudowords (or "novel words," i.e., letter strings resembling real words but lacking assigned meanings). Semantic similarity between pseudowords were established through a distributional semantic model able to represent in a vector space, not only attested words but also unmapped strings as bags of character n-grams. Participants were instructed to memorize those lists and then to perform a recognition task. Results showed that participants false and veridical recognition increased with increasing semantic similarity between each stimulus and the stimuli comprising its list, as estimated by the distributional model. These findings extend previous evidence indicating that humans are sensitive to the semantic (distributional) patterns elicited by novel words by showing that this sensitivity can even induce humans to falsely recognize stimuli that they have never encountered in their entire lives.

Daytime naps consolidate Cantonese tone learning through promoting cross-talker perception: The role of prior knowledge.

Daytime naps consolidate Cantonese tone learning through promoting cross-talker perception: The role of prior knowledge.

Modality matters: how combining oral and written instructional explanations improves STEM learning from video lectures

Background and purpose of the studySTEM learning often involves a multitude of complex and abstract concepts and ideas that can be challenging for students to comprehend. Research suggests that the oral and visual representations in video lectures can maximize students’ cognitive infrastructure, helping them to organize knowledge more effectively. However, compared to traditional learning methods, video lectures may lack interaction and feedback, which can lead to ineffective learning strategies (e.g., passive viewing) and reduced learning engagement. Instructional explanations serve as a generative strategy, enabling students to create oral and written pieces based on the knowledge gained from video lectures and their prior knowledge. This study recruited a total of 87 undergraduate students and explored how the modality of instructional explanations generated by these students for a fictious student influenced their learning. Specifically, the study explored the effects on students’ learning performance, attention, behavioral patterns of preparing-to-explain, the quality of notes, and the quality of instructional explanations in video lectures on a STEM subject.ResultsThe results revealed that students who adopted a combination of oral and written instructional explanations showed better immediate retention and transfer than those adopted just one type of explanation. In addition, both oral-only and combined oral-and-written explanations promoted more self-regulated learning behaviors during the phase of preparing-to-explain. The study also found that the quality of instructional explanations played a mediating role in the effects of modality.Conclusions and potential implicationsOur findings suggest that combining oral and written instructional explanations is more effective in supporting students’ STEM learning from video lectures compared to using a single form of explanation. These findings have significant implications for teaching and learning STEM subjects through video lectures. Students and educators should recognize the complementary roles of oral and written instructional explanations and opt for a combined oral-and-written approach during STEM learning activities.

Observers translate information about other agents' higher-order goals into expectations about their forthcoming action kinematics.

Observers translate information about other agents' higher-order goals into expectations about their forthcoming action kinematics.

A data driven approach to urban area delineation using multi source geospatial data

This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which represents a significant improvement in precision and methodology compared to traditional approaches that rely on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The robustness of the proposed method is validated through a comprehensive assessment involving three key elements: (1) a 5% improvement in average accuracy, (2) optimal clustering selections based on entropy values that eliminate the need for prior knowledge, and (3) validation through nighttime light data and Zipf’s law, where a high p-value of 0.99 confirms a good fit, supporting the power law. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban areas in varied settings.

Evaluation of the information transmission of treatment with dental aligners in social media: empathetic language versus non-empathetic language

BackgroundInformation presented in audiovisual format, is more effective in the retention of information by patients than traditional written methods. On the other hand, empathy in the transmission of information is a crucial element in the relationship between health professionals and patients. This study aimed to determine whether the presentation of information in audiovisual format in an affective and empathetic way, as is done in social networks, improves the knowledge acquired by orthodontic patients treated with aligners.Methods60 participants, which were equally randomized in 2 groups, took part in this pilot study. Two videos were created that delivered the same informative content about treatment with dental aligners. One video used non-technical and empathetic language, for the level of understanding of patients (empathic video: EG), and the other used technical (similar to that used in clinical practice) but non-empathetic language (non-empathetic video: N-EG). Thirty randomized participants watched the empathic video and another thirty watched the non-empathetic video, and then completed the same questionnaire to compare the information acquired. The relative frequencies of correct responses in each group, were analysed by means of cross-tabulations. The significance of the differences between the results of the two groups was calculated with the Chi-square test.ResultsThe questionnaire confirmed that 70% of the participants had no prior knowledge of aligners. The 66.7% of patients, had no relatives or close friends or family who had been previously treated with aligners. Also 75% of them, stated that they had not previously searched for information about aligners on the Internet. After viewing the videos, all patients were able to respond adequately to most of the questions asked, regardless of whether they had viewed the empathic (EG) or non-empathetic (N-EG) video. Significant differences (p < 0,05) were recorded in 2 questions classified as technical difficulty, in which the EG obtained better results in terms of knowledge acquired than the N-EG.ConclusionsShowing audiovisual format information to patients, improves patients’ understanding of orthodontic treatment with dental aligners. Empathy enhances acquired knowledge for unusual and unknown information.

Learning the Difference of Few-Shot Food Data Using Multivariate Knowledge-Guided Variational Autoencoder.

Recent advancements in food image recognition have underscored its importance in dietary monitoring, which promotes a healthy lifestyle and aids in the prevention of diseases such as diabetes and obesity. While mainstream food recognition methods excel in scenarios with large-scale annotated datasets, they falter in few-shot regimes where data is limited. This paper addresses this challenge by introducing a variational generative method, the Multivariate Knowledge-guided Variational AutoEncoder (MK-VAE), for few-shot food recognition. MK-VAE leverages handcrafted features and semantic embeddings as multivariate prior knowledge to strengthen feature learning and feature generation in different phases. Specifically, we design a lightweight and flexible feature distillation module that distills handcrafted features to enhance the feature learning network for capturing the salient visual information in few-shot samples. During the feature generation phase, we utilize a variational autoencoder to learn the difference distribution of food data and explicitly boost the latent representation with category-level semantic embeddings to pull homogeneous features closer together while pushing inhomogeneous features apart. Experimental results demonstrate that our proposed MK-VAE significantly outperforms state-of-the-art few-shot food recognition methods in both 5-way 1-shot and 5-way 5-shot settings on three widely-used benchmark datasets: Food-101, VIREO Food-172, and UECFood-256.

Evaluating the impact of the aerosol sampling time interval on CWT and PSCF source-receptor models: A critical perspective.

Evaluating the impact of the aerosol sampling time interval on CWT and PSCF source-receptor models: A critical perspective.

Active Learning Attraction Basins of Dynamical System

Abstract Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions. A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system states. Such a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state space. However, determining the attractor for each initial state can be a laborious task. Here, we tackle the challenge of reconstructing attraction basins using as few initial points as possible. In each iteration of our approach, informative points are selected through random seeding and are driven along the current classification boundary, promoting the eventual selection of points that are both diverse and enlightening. The results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping accuracy. Additionally, the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

A Human-Like Reading Method for Pointer Meter Based on Scale Values Recognition and Meter Elements Segmentation

Abstract Due to the simple structure and strong anti-interference ability of pointer meters, they play a crucial role in complex industrial environments. To address the limitations of existing intelligent reading methods, which rely on prior knowledge of scale values, are not adaptable to various types of meters, involve difficulties in meter elements extraction, and are inefficient in reading processes, this paper proposes a human-like reading method for pointer meter based on scale values recognition and meter elements segmentation. Specifically, the YOLODIG algorithm is proposed for the identification of the primary scale values in the scale reading phase, thereby addressing the challenge of scale value a priori, which is applicable to a variety of pointer meters. In the subsequent stage of meter elements extraction, we propose the CL-Multi-U2Net multi-class semantic segmentation network to segment the pointer lines and primary scale lines. Our proposed Cross-Scale Aggregation Interaction Module (CSAIM) effectively integrates multi-scale information across layers, increasing the penalty for edge contour segmentation by redesigning the loss function to improve the accuracy of meter elements segmentation. Of particular significance is the proposal of the Human Eye Simulation Reading Method (HESRM) in the reading stage to calculate precise reading values. The HESRM has the advantage of effectively reducing cumulative error and improving reading efficiency and recognition accuracy. The experimental results demonstrate that the YOLODIG algorithm attains an SRM rate of 95.61. The MIOU and F1-score of the CL-Multi-U2Net meter elements segmentation network achieve 86.64 and 92.56, respectively, representing improvements of 0.67 and 0.38 compared to the pre-optimization phase. The HESRM proposed in this research attains an average reference error rate of only 0.245%, which is the lowest in comparison with existing methods and is more suitable for practical applications.

Exploring students’ prior knowledge of and metacognitive skills for game-based learning in relation to cognitive strategy and learning effectiveness

Exploring students’ prior knowledge of and metacognitive skills for game-based learning in relation to cognitive strategy and learning effectiveness

Menstrual Hygiene Management among School Girls

In Nepal, menstruating women are considered impure, and adolescent girls are not allowed to do household chores and restrict to participate in religious or cultural events during menstruation. Menstruation is an important event in the life of an adolescent girl, which indicates that signs of maturity. Detail information about menstruation that begins in early adolescence will improve safe practices and alleviate suffering for millions of women. Quantitative data were collected by self-administered questionnaire was completed by schoolgirls in four schools, two were private and two were community. A survey was conducted among adolescent girls (10-19 years) residing in Banke District. Four schools were selected purposively. A purposive sample of 100 female students were selected from grades 9 and 10 from the out of 233 students (Grade 9 = 10, Grade 10 = 15, total 25) was selected by quota sampling method. The questionnaire survey was used as a research tool and data collection was obtained consent from both school principals and respondents before taking the data. The data were analyzed using a simple frequency table and to interpret the results descriptively. Study found that Shyness, high costs and prices, lack of prior knowledge, and unavailability in local shops among others were significant factors that were not promoting girls to use disposable sanitary pads which neglected the Menstrual hygiene management. Despite the cultural stigma on menstruation, a considerable number of girls attend the regularity to schools, with or without notice if they are on their periods.