Search Results (798)

This paper explores the intersection of embodied trauma, resilience, and healing as represented in contemporary art, focusing on a case study analysis of the autoethnographic practice as a reflexive methodology that integrates personal lived experience with cultural, political, and artistic analysis of the works of Zlatar. Central to this study is examining the notion of rematriation, which calls for the reclamation of women’s histories and the restoration of knowledge passed down through generations. Through a series of her paintings, including works from her series A Serbian Renaissance, Refuge For the Oppressed Body, and The Minotaur Came and I Surrendered, Zlatar interrogates the transmission of trauma across generations of women, from Balkan origins, focusing on issues such as gender-based violence, displacement, and identity formation. These works challenge dominant narratives by centring women’s experiences not through externalized indicators or representations of healing, but mediating how mind–body relationships have dialogue, and her art employs this concept as spaces for memory, survival, and meaning-making. Drawing on feminist philosophy, artwork analysis and trauma studies, this paper situates Zlatar’s art to address historical inequities in women’s healing and the ongoing struggle for women’s agency and safety in contemporary society. Full article

To address the strong nonlinearity of aerodynamic loads during projectile exterior ballistic flight and the difficulty in accurately modeling fuze dynamic responses, this paper proposes a data-driven modeling and simulation method for fuze exterior ballistic dynamics. A high-fidelity aerodynamic database covering a range of Mach numbers and angles of attack is constructed based on CFD (Computational Fluid Dynamics) simulations. An MLP (Multilayer Perceptron) neural network is then employed to develop an aerodynamic surrogate model, enabling continuous representation of aerodynamic loads within the given sample space. The results show that, within the data coverage range, the proposed model is able to capture the nonlinear variation in aerodynamic parameters and shows improved prediction accuracy compared with the polynomial fitting method. Specifically, for typical aerodynamic parameters, the RMSE (Root Mean Square Error) is reduced from 5.758 to 0.223, the MAE (Mean Absolute Error) is reduced to 0.099, and the R² (Coefficient of Determination) approaches 1. On this basis, the aerodynamic surrogate model is embedded into a six-degree-of-freedom projectile–fuze exterior ballistic dynamics model via the secondary development interface of ADAMS 2020 (Automated Dynamic Analysis of Mechanical Systems), enabling coupled simulation between aerodynamic loads and multibody dynamics. Comparison with firing table data indicates that, under typical operating conditions, the relative deviation of ballistic parameters is generally better than 94%, demonstrating that the proposed method can reasonably reproduce the projectile exterior ballistic characteristics. Furthermore, based on the coupled dynamics model, the dynamic response characteristics of the fuze moving body during the exterior ballistic phase are analyzed. The results indicate that the axial forward overload of the moving body increases significantly with the initial nutation angle, and the variation in the axial projection of gravity induced by nutation plays an important role in its transient response. The proposed approach provides a useful reference for the dynamic response analysis and safety evaluation of fuzes. Full article

Most computational studies of vehicle hydroplaning have emphasized structural realism through fluid–structure interaction, tire deformation, tread geometry, and pavement surface characterization. By contrast, the hydrodynamics governing the flow in the tire vicinity, particularly the role of turbulence, have received comparatively limited attention. In a significant number of studies, the flow has been treated as laminar despite turbulent flow conditions, while in a few other studies turbulence modeling has been adopted without an explicit assessment of its impact on hydroplaning predictions. In this study, we present a simplified three-dimensional computational fluid dynamics (CFD) model designed to isolate the flow regimes governing hydroplaning and to quantify the mean effect of the turbulence modeling on the predicted hydroplaning speed. Using a finite-volume formulation with a volume-of-fluid representation of the air–water interface, the flow around and beneath a smooth 0.7 m-diameter tire sliding in locked-wheel mode over a flooded, nominally smooth pavement is simulated. The tire is represented as a rigid body with an idealized rectangular bottom patch whose area is determined from the tire load and inflation pressure, avoiding the need to prescribe a measured or assumed deformed footprint. Steady-state hydroplaning is modeled for a uniform upstream water film thickness of 7.62 mm with a 0.5 mm gap between the tire and the pavement, over tire inflation pressures ranging from approximately 100 to 300 kPa, and predictions are verified against the empirical NASA hydroplaning equation. For these conditions, simulations without turbulence closure exhibit a consistent, systematic underprediction of the hydroplaning speed of approximately 13.5% relative to the NASA relation. Incorporating turbulence effects through Reynolds-averaged closures substantially reduces this bias, with average deviations of about 6% for the realizable k–ε model and 2.4% for the shear stress transport (SST) k–ω model. An analysis of the results indicates that hydrodynamic lift is dominated by pressure buildup associated with stagnation at the lower leading edge of the tire, with a significant contribution from shear-dominated flow in the thin under-tire gap, and that turbulence acts to moderate the integrated lift from these pressure fields. These results demonstrate that explicitly accounting for turbulence in the tire vicinity is essential for reproducing empirical hydroplaning trends and for avoiding systematic bias in CFD-based hydroplaning predictions. Full article

Computational thinking (CT) has become a cross-curriculum priority in many educational jurisdictions, yet a growing body of research reports uneven integration in initial teacher education (ITE), limited preservice teacher confidence, and persistent misconceptions that equate CT with coding. Concurrently, generative artificial intelligence (GenAI) has rapidly entered university programmes, offering new possibilities for modelling problem-solving, generating multiple representations, and supporting iterative design. However, while constructs such as self-efficacy, cognitive load, and affect are well established in educational psychology, their specific application to the intersection of CT and GenAI in teacher education remains under-theorised: existing research has not systematically examined how these psychological dimensions interact when preservice teachers learn CT through GenAI-mediated tasks. This thematic literature review synthesises 54 sources across three intersecting domains: CT frameworks and their pedagogical implications, CT integration in preservice teacher preparation, and GenAI in teacher education and learning design. Drawing on Bandura’s social cognitive theory, cognitive load theory, and research on technology-related affect, the review foregrounds the affective, cognitive, and cultural dimensions of preservice teachers’ engagement with CT and GenAI. The review proposes the GenAI-Enabled Computational Thinking for Preservice Teachers (GECT-P) model, which integrates CT dimensions with GenAI-supported learning cycles, psychological mediators, and teacher education outcomes. The model positions prompting as an epistemic and pedagogical practice that can make CT visible, supports cycles of decomposition, abstraction, pattern recognition, and algorithmic design, and embeds critical AI literacy, ethics, affective scaffolding, and classroom enactment. Design principles and practical pathways are offered for teacher educators seeking to prepare graduates who can develop CT with and beyond GenAI across diverse curriculum areas. Full article

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

Soybean (Glycine max [L.] Merr.) is a globally important crop with a rapidly expanding body of genomics literature driven by advances in sequencing and functional genomics. Thousands of studies reference soybean genes using standardized Glyma identifiers; however, systematic analyses of how these identifiers are distributed across chromosomes in the scientific literature remain limited. Here, we present a chromosome-resolved bibliometric analysis of soybean gene mentions using a reproducible rule-based text mining approach. PubMed abstracts published between December 2006 and December 2025 were mined for standardized Glyma gene identifiers using regular-expression-based entity extraction. A total of 377 PubMed records were retrieved, of which 340 abstracts (90.2%) contained at least one Glyma gene identifier. The median number of unique genes mentioned per abstract was 1, with a maximum of 14 genes reported in a single study. Our results reveal three major patterns. First, soybean genomics research remains predominantly gene-centric, with most abstracts referencing one or two genes. Second, apparent chromosome-level disparities exist in literature representation within the subset of studies using standardized Glyma identifiers, with chromosomes 3 and 16 exhibiting the highest frequencies of unique gene mentions. A Chi-square goodness-of-fit test confirmed that these differences deviate significantly from a uniform distribution (χ² = 123.71, p < 0.001), indicating non-random patterns of gene reporting. Third, a small subset of genes dominates the literature, while the majority of annotated genes are mentioned infrequently, reflecting a long-tailed distribution of research attention. This analysis captures reporting patterns in studies that explicitly use standardized Glyma identifiers and therefore represents a defined subset of the broader soybean genomics literature. Within this scope, the findings highlight uneven adoption of standardized gene nomenclature and chromosome-level differences in research emphasis. More broadly, this study demonstrates the utility of transparent, rule-based text mining approaches for large-scale bibliometric analyses in plant science and provides a scalable framework for comparative analyses across crop species. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Visualising data is a key analytical and communicative tool, particularly for complex phenomena such as school dropout. This article examines how national and international educational bodies (e.g., UNESCO, the EU and the Italian Ministry of Education) depict dropout, highlighting issues stemming from non-standard definitions, heterogeneous indicators and incomplete data. These limitations reduce the effectiveness of such representations, which often fail to capture the phenomenon’s dynamic nature or guide timely interventions. The article stresses the need to improve data accessibility through clearer communication and to adopt longitudinal approaches enabling more accurate tracking of educational trajectories, thereby supporting more effective educational policies. Full article

The skyhook inertance (SHI) control strategy facilitates the real-time adaptation of inertance parameters to dynamic loading conditions, consequently enhancing vehicle ride comfort. It features a simple algorithm and strong robustness. However, traditional skyhook inertance systems only adjust the magnitude of the control force by changing the inertance, without regulating the control force phase, which limits the control effect of the SHI control strategy. To solve this problem, this study introduces a fractional-order skyhook inertance (Fo-SHI) control approach. This method substitutes the second-order differential terms appearing in the conventional equation of motion of the fractional-order skyhook inertance system with fractional-order derivatives of the displacement. Consequently, the proposed strategy enables continuous and independent tuning of both the amplitude and phase of the generated control force. To achieve a realistic representation of the Fo-SHI forces, a fractional-order model integrating an adjustable damper and an inerter was developed. This model was subsequently validated through prototype testing, and its parameters were identified via a fitting process. The results of Hardware-in-the-Loop experiments demonstrate that the semi-active suspension employing the Fo-SHI control strategy achieves significant performance improvements over the conventional SHI-controlled suspension: the root mean square of body acceleration is reduced by up to 18.12% under full-load conditions, while suspension working space and dynamic tire load also show favorable responses. These findings clearly underscore the advantages and rationale for incorporating fractional-order control into vehicle suspension systems. Full article

Automated recognition of equine–human interaction behaviors from video represents a significant challenge in computational ethology, with critical applications spanning animal welfare assessment, equine-assisted services evaluation, and safety monitoring in equestrian environments. Existing approaches to animal behavior recognition typically focus on single species in isolation, rely solely on facial expression analysis while ignoring full-body posture, or employ flat classification architectures that fail under the severe class imbalances characteristic of naturalistic behavioral datasets. Furthermore, no prior framework integrates simultaneous analysis of both human and equine body language for cross-species interaction classification. This paper presents a novel hierarchical classification framework integrating multi-modal computer vision features to distinguish behavioral states during horse–human encounters. Our methodology employs three complementary feature extraction pipelines: YOLOv8 for spatial relationship modeling, MediaPipe for human postural analysis, and AP-10K for equine body language interpretation. From 28 annotated interaction videos comprising 50,270 temporal samples across five horse breeds, we extract 35 discriminative features capturing proximity dynamics, body orientation, and species-specific behavioral indicators. To address severe class imbalance (18.3:1 ratio between affiliative and avoidant categories), we implement cost-sensitive gradient boosting with automatic class weight optimization within a two-stage hierarchical architecture. The first stage classifies interactions into three parent categories (affiliative, neutral, avoidant) achieving 73.2% balanced accuracy, while stage two discriminates six fine-grained sub-behaviors achieving 88.5% balanced accuracy (under oracle parent-category routing; cascaded end-to-end performance is 62.9% balanced accuracy due to Stage 1 error propagation, identifying parent classification as the primary bottleneck). Notably, our system achieves 85.0% recall on safety-critical avoidant behaviors despite their representation of only 3.8% of the dataset. Extensive ablation studies demonstrate that equine pose features contribute most critically to classification performance, while comprehensive cross-validation analysis confirms model robustness across diverse interaction contexts. The proposed framework establishes the first systematic multimodal cross-species behavioral assessment pipeline in human–animal interaction research, with direct implications for improving equine welfare monitoring and rider safety protocols. Full article

Elementary music teachers in the United States face many challenges today, including an increasing cultural divide between teachers and students, worsening student behavior, and excessive exposure to technology in children’s lives. These challenges are magnified due to the hundreds of students elementary music teachers see weekly, the lack of teaching and planning time, and inadequate teaching resources, making it difficult to fully understand the culture and learning needs of every child. However, music educators may find culturally sustaining pedagogy (CSP) a useful tool for meeting the needs of a diverse student body. Further, when teachers engage in kid culture, the environments and activities that only children have, there is a plethora of music and media to use that children prefer that can help increase engagement and reduce behavioral problems. In this Perspective article, I provide three sample lessons that model instructional strategies that challenge current systems of power and representation in music education and center student agency through singing, chanting, moving, playing, and creating. Using repertoire that students already know and prefer, such as “Old Town Road,” Fortnite dances, and the song “See You Again”, draws from children’s funds of knowledge. Moving away from the Western art music canon and traditional formal education structures (like standard notation) in favor of learning by ear, peer collaboration, and improvisation decolonizes the curriculum. Critical reflexivity occurs when the teacher acts as a learner, constantly adjusting lessons to ensure student agency and addressing ethical issues, such as the intellectual property rights of creators whose work is used in media like Fortnite. By using melodies, songs, and video game movements children already know, music teachers can use the materials and learning processes in kid culture to engage in culturally sustaining pedagogy. I aim to inspire educators and researchers to reflect on sustaining children’s dynamic, cultural practices and better understand how to authentically bring popular music and media into elementary music lessons to provide a more engaging, relevant, and transformative music education. Full article

This study builds on a small body of work, all on Japanese, demonstrating how intonational phonology is critical for understanding prosodic modifications in infant-directed speech (IDS) relative to adult-directed speech. We performed similar analyses on simulated infant-directed speech vs. reading of a story in English and Bengali: two languages that – unlike Japanese – both have stress and do not use fundamental frequency (F0) to signal changes in word-level meaning, but that have two very different intonational grammars. These differences allowed us to disentangle previous hypotheses about intonational exaggeration in IDS being concentrated in a particular part of the melody. We tested hypotheses that state this locus of exaggeration is either at: the final position in the melody (final in the intonational phrase), the most unpredictable part of the melody, or in pragmatically informative tones. Our results support the first hypothesis. We found that the phonological choices of speakers to chunk the story into shorter, larger prosodic constituents drive intonational exaggeration in IDS. This is because the intonational phrase-final position in both languages is the site of greatest pre-boundary lengthening and F0 range expansion. We also demonstrate: (i) quantification of predictability in intonational melodies using probabilistic finite state automaton representations of intonational grammars and (ii) F0 statistical analyses that are robust and scalable to large, naturalistic IDS corpora. Full article

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H²Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H²Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article

This study analyses two wetlands within the Medina del Campo groundwater body (Duero River Basin, Spain) to reconstruct flood patterns and quantify the hydrological volumes involved in episodic inundation. We integrate Sentinel satellite imagery (2015–2024), targeted field campaigns (2024–2025), and preliminary water-balance assessments (2015–2022). Calculations were constrained to the inundated cells of each wetland bed to reduce spatial heterogeneity issues. For Laguna de los Lavajares, an initial standing water depth was assumed to estimate infiltration losses more accurately. We discuss the primary sources of uncertainty—particularly the representation of atmospheric losses as evaporation versus evapotranspiration—and recommend computing water balances for wet, average, and dry years to capture interannual variability. Key findings include the identification of distinct hydroperiods for each wetland, the dominant role of infiltration in the water balance of Laguna de los Lavajares, and the critical influence of vegetation-driven evapotranspiration in Laguna Redonda. Full article

Background: Misperception of body weight has been found to negatively impact both diet and physical activity levels, particularly in youth with overweight and obesity. Objectives: This study assessed consistency between actual and perceived weight status and lifestyle factors in a sample of 455 children and adolescents (55% males, 8–13 years) attending a summer camp in Northern Italy. Methods: Weight status was defined applying Body Mass Index (BMI) cut-offs. Adherence to the Mediterranean Diet (MD), physical activity level, sleep duration, and sleep quality were assessed through validated questionnaires. Self-perception was evaluated through 5-point Likert scales, with graphical representations. Results: Comparison between self-perceived and assessed parameters revealed a poor concordance across all types of variables. Approximately half of participants (43–55%) correctly rated their weight status (κ = 0.12; 95% CI: 0.05–0.19), diet quality (κ = 0.09; 95% CI: 0.02–0.15), physical activity level (κ = 0.18; 95% CI: 0.11–0.26), sleep time (κ = 0.10; 95% CI: 0.03–0.17), and sleep quality (κ = 0.18; 95% CI: 0.12–0.24). Participants 12–13 years old were more likely to have a greater weight status perception compared to younger subjects (OR = 2.13; 95% CI: 1.08–4.21). Being in a condition of overweight or obesity significantly decreased the odds of correct weight perception (OR = 0.13; 95% CI: 0.08–0.21). Similarly, subjects with higher adherence to the MD, adequate sleep time, and low sleep quality were more conscious about their diet and sleep patterns. Conclusions: Overall, these findings highlight a certain degree of misclassification, especially in subjects who need to improve their lifestyles, highlighting the potential relevance of fostering accurate self-perception during developmental age. Full article