11237 research outputs found
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Teaching and learning outdoor adventure activities within initial teacher education
No full textEnglish schools often adopt traditional Physical Education, which can limit pupil development, exclude less capable students, and lack real-life relevance. Outdoor Adventurous Activities (OAA) offer an alternative through experiential learning, collaboration, problem-solving, nature connection, and risk management. Since OAA is compulsory, teacher preparation is essential. However, little is known about whether initial teacher education (ITE) equips pre-service teachers (PSTs) with the required knowledge, skills, and confidence. This study, using Occupational Socialisation Theory, explored influences on PSTs’ learning and teaching of OAA during ITE, focusing on school placements. A case study included interviews with 13 PSTs (five Generalists, eight PE Specialists) on a postgraduate teacher education course and two university staff. Findings showed limited school opportunities to practice PE and OAA, reliance on residentials and external providers, PE specialists’ confidence despite limited knowledge, and generalists’ lack of preparation. The study informs teacher education, outsourcing research, and professional development in OAA.</p
The Relationship Between Adverse Childhood Experiences and Elder Abuse in Turkiye
No full textNo description supplied</p
Robust Diverse Multi-view Learning for Cancer Subtyping
No full textCancer subtyping is crucial for categorizing patients into distinct groups, enabling precision medicine and personalized therapies. As multi-omic analysis becomes more prevalent, integrating data from various omics provides deeper insights into the potential relationships between cancer subtypes. Although most cancer subtyping methods show promising performance, they have several limitations. These methods fail to account for omic differences, address noise in similarity matrices, and preserve the manifold structure of high-dimensional data in lowdimensional space. This study proposes a Robust Diverse Multiview Learning (RDML) model for cancer subtyping. Specifically, multi-view self-representation matrices are formulated as a thirdorder tensor. Differences between views are captured using an orthogonal diversity term, thereby reducing the redundant information between views. To enhance robustness of model to noise, we explicitly separate the self-representation tensor into a clean tensor and a noise tensor. Additionally, Laplacian manifold regularization is employed to preserve the local structure of highdimensional data in low-dimensional space. An efficient algorithm is designed to solve the proposed model. Comprehensive experiments are conducted on ten datasets, demonstrating the superior performance of the proposed model.</p
MacaqueNet: advancing comparative behavioural research through large‐scale collaboration
No full textThere is a vast and ever‐accumulating amount of behavioural data on individually recognised animals, an incredible resource to shed light on the ecological and evolutionary drivers of variation in animal behaviour. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible and standardised databases. Big‐team approaches to building standardised databases offer a solution to facilitating reliable cross‐species comparisons. By sharing both data and expertise among researchers, these approaches ensure that valuable data, which might otherwise go unused, become easier to discover, repurpose and synthesise. Additionally, such large‐scale collaborations promote a culture of sharing within the research community, incentivising researchers to contribute their data by ensuring their interests are considered through clear sharing guidelines. Active communication with the data contributors during the standardisation process also helps avoid misinterpretation of the data, ultimately improving the reliability of comparative databases. Here, we introduce MacaqueNet, a global collaboration of over 100 researchers (https://macaquenet.github.io/) aimed at unlocking the wealth of cross‐species data for research on macaque social behaviour. The MacaqueNet database encompasses data from 1981 to the present on 61 populations across 14 species and is the first publicly searchable and standardised database on affiliative and agonistic animal social behaviour. We describe the establishment of MacaqueNet, from the steps we took to start a large‐scale collective, to the creation of a cross‐species collaborative database and the implementation of data entry and retrieval protocols. We share MacaqueNet's component resources: an R package for data standardisation, website code, the relational database structure, a glossary and data sharing terms of use. With all these components openly accessible, MacaqueNet can act as a fully replicable template for future endeavours establishing large‐scale collaborative comparative databases.</p
Measuring state functionality appreciation: a psychometric evaluation of an adapted version of the functionality appreciation scale (S-FAS)
No full textWithin the body functionality and body image research, there is an absence of psychometrically validated measures for capturing functionality appreciation as a state-like construct. To address this, we conducted an extensive psychometric analysis of a state version of the Functionality Appreciation Scale (FAS; Alleva et al., 2017), initially offered by Alleva et al. (2024b), across two online studies. Exploratory factor analyses among a UK-based community sample of 583 adults (18–85 years; age M = 34.66) led to the extraction of a 7-item, unidimensional model of S-FAS scores, which presented adequate composite reliability and good patterns of construct validity (i.e., convergent, concurrent, incremental). Using confirmatory factor analyses, we cross-validated the optimal model among a second community sample of 295 adults (18–78 years; age M = 38.65) from the United Kingdom. Results showed that the unidimensional model of S-FAS scores had adequate fit, demonstrated discriminant validity, and provided additional evidence of measurement invariance (up to latent mean level) across gender identity (women, men) and time (i.e., at pre-test and post-test). Our findings further showed that the S-FAS is sensitive to experimental manipulation and thus accurately captures changes in state functionality appreciation. Overall, the S-FAS is a psychometrically valid measure for assessing functionality appreciation as a state-like construct in future research and practice.</p
A comparative analysis of eye movement accuracy for locating items held in visual short‐term memory among young healthy adults, older adults with normal cognition, and older adults indicative of mild cognitive impairment
No full textBackground: We compared the accuracy of eye movements in locating an item stored in visual short‐term memory between young healthy adults, normally aging older adults, and older adults with mild cognitive impairment as indicated by the Montreal Cognitive Assessment or Addenbrooke's Cognitive Examination‐III test. Methods: Thirty‐three young healthy adults, 38 normally aging older adults, and 17 older adults indicative of MCI completed two experiments requiring object‐location binding. In Experiment 1, participants viewed 2–4 memory items displayed sequentially at random screen locations. Following a 900 ms interval, eye movements were recorded while participants moved their eyes to the location of the memory item corresponding to a displayed cue. In Experiment 2 (control), participants indicated whether or not the test item was shown at its original location using a yes/no response. Results: MCI‐indicative participants exhibited greater saccadic error (spatial deviation of saccadic endpoint from the remembered target location) than normally aging older ( p = 0.002) and young ( p Conclusion: Saccadic accuracy declined with memory load for all groups. The MCI‐indicative group showed lower saccadic accuracy versus normally aging older and young adults at low memory load. The findings offer important insights into our understanding of saccadic eye movement as a potential behavioral marker for MCI.</p
The Mediating Effect of Institutional Governance on Banking Depth and Economic Performance
No full textThis study examines the impact of banking depth on economic performance in 47 Asian economies from 1980 to 2022, with a focus on the mediating role of institutional governance. Using feasible generalized least squares (FGLS) models and structural equation modelling (SEM), we find that banking depth positively affects both economic development (GDP per capita) and growth (ΔGDPPC). Institutional governance—measured by government effectiveness, regulatory quality, and political stability—enhances this relationship, underscoring the importance of institutional context in Asia. Specifically, control of corruption, rule of law, and voice and accountability fully mediate the impact of banking depth on economic growth, suggesting that improvements in banking depth alone are insufficient without strong institutional support. Additionally, the results highlight the nuanced role of institutional quality across different income groups: while economic growth in higher-income countries is broadly supported by institutional quality, these same governance structures may not optimally enhance the positive impacts of banking depth and size development on the economy. In contrast, the effects of banking on growth in lower-income countries become volatile when institutional quality is considered, emphasizing the need for targeted institutional reforms. Our findings contribute to the existing literature and highlight the need for tailored institutional reforms to maximize the economic benefits of financial sector development and institutional strengthening in Asia.</p
Knowing What You Don’t Know: Why Professional Doctorate Students Should Tread Carefully with AI Research Assistants
No full textThe integration of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, into academic research is accelerating. While these tools offer considerable utility for professional doctorate students—especially those engaged in practice-based, qualitative inquiry—their use also presents serious epistemological, ethical, and pedagogical challenges. This article critically examines the promise and limitations of AI in the context of professional doctorates, with a specific focus on qualitative research. Drawing from recent scholarship, it highlights how overreliance on AI can bypass crucial aspects of intellectual development, compromise reflexivity, and obscure researcher accountability. This paper argues for a principled and transparent use of AI, guided by structured frameworks, to ensure that the human researcher remains central to meaning-making and scholarly authorship. The proposed stance foregrounds epistemic agency and underscores the importance of learning through complexity and discomfort in the doctoral journey.</p
Federated fuzzy C-Means for multi-layer network community detection in industrial Internet-of-things
No full textMulti-layer network community detection is a crucial topic in Industrial Internet of things(IIoT). Due to communication and privacy requirements, network data is distributed across multiple devices, being a significant challenge to develop a model to learn latent information for community detection. To address the problem, this paper proposes a federated fuzzy C-Means for multi-layer network community detection. Firstly, non-negative matrix factorization is employed to obtain a low-dimensional representation via training local data in each client. The gradients of the global centroids are then transmitted to a central server for consistent fusion and complete community detection within the fuzzy C-Means framework. As a result, the training process for each client remains independent and leads to effectively privacy preservation. Experimental results demonstrate that the proposed method can successfully perform multi-layer network community detection across distributed devices and achieve comparable performance in contrast with centralized community detection methods on four public datasets.</p
Trustworthy neighborhoods mining: homophily-aware neutral contrastive learning for graph clustering
No full textRecently, neighbor-based contrastive learning has been introduced to effectively exploit neighborhood information for clustering. However, these methods rely on the homophily assumption-that connected nodes share similar class labels and should therefore be close in feature space-which fails to account for the varying homophily levels in real-world graphs. As a result, applying contrastive learning to low-homophily graphs may lead to indistinguishable node representations due to unreliable neighborhood information, making it challenging to identify trustworthy neighborhoods with varying homophily levels in graph clustering. To tackle this, we introduce a novel neighborhood Neutral Contrastive Graph Clustering method NeuCGC that extends traditional contrastive learning by incorporating neutral pairs-node pairs treated as weighted positive pairs, rather than strictly positive or negative. These neutral pairs are dynamically adjusted based on the graph's homophily level, enabling a more flexible and robust learning process. Leveraging neutral pairs in contrastive learning, our method incorporates two key components: 1) an adaptive contrastive neighborhood distribution alignment that adjusts based on the homophily level of the given attribute graph, ensuring effective alignment of neighborhood distributions, and 2) a contrastive neighborhood node feature consistency learning mechanism that leverages reliable neighborhood information from high-confidence graphs to learn robust node representations, mitigating the adverse effects of varying homophily levels and effectively exploiting highly trustworthy neighborhood information. Experimental results demonstrate the effectiveness and robustness of our approach, outperforming other state-of-the-art graph clustering methods. Our code is available at https://github.com/THPengL/NeuCGC</p
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