Search Results (2,036)

In this paper, by means of a comprehensive analysis of the statutory and non-statutory documents that govern its preschool provision, we examine how early childhood education and care (ECEC), particularly in relation to mathematics, is conceptualised by the English educational authorities. Situated within international debates about economic (school-readiness, accountability-driven) versus social (holistic, play-based, rights-oriented) models of ECEC, the study explores how curriculum expectations, assessment practices and didactical guidance collectively frame young children’s learning opportunities. Drawing on a document-based analytic approach, and guided by six literature-derived questions, the analysis reveals significant inconsistencies both within and between documents, including conflicting messages about the purpose of preschool, an uneven emphasis on school readiness, and ambivalent statements regarding the role of play, instruction and practitioner agency, as well as contradictory and shifting expectations surrounding the scope, status and pedagogical treatment of early mathematics. While statutory materials frequently privilege school readiness and narrowly defined number outcomes, non-statutory guidance promotes broader mathematical thinking, exploratory play and child-initiated reasoning. Overall, the findings demonstrate limited coherence across the English authorities’ ECEC expectations and highlight the interpretive and professional challenges faced by practitioners expected to implement this fragmented early years mathematics policy landscape. Full article

Background/Objectives: Dietary behavior in eating disorders (EDs) is often framed through either nutritional or psychological perspectives, yet emerging evidence suggests that eating may involve a transdiagnostic, emotionally embedded, and relationally negotiated process. While research highlights the role of emotion regulation difficulties, perfectionism, control, and overvaluation of weight and shape in ED maintenance, less is known about how these processes are interpreted and managed in clinical practice across different cultural contexts. This study explored psychotherapists’ perspectives on dietary behavior, nutritional assessment, and interdisciplinary collaboration in ED treatment in Greece and the United Kingdom. Methods: Eighteen psychotherapists (9 Greek and 9 British) with experience in treating individuals with EDs participated in in-depth semi-structured interviews. Data were analyzed using reflexive thematic analysis. Results: Three themes were developed. First, therapists conceptualized dietary behavior as reflecting broader transdiagnostic psychological processes, particularly perfectionism, control, emotion regulation difficulties, and body image concerns. Second, nutritional assessment and intervention (e.g., food diaries and meal plans) were experienced as emotionally significant practices that required negotiation of timing, meaning, and clients’ readiness for change. Third, interdisciplinary collaboration was described as involving ongoing negotiation of nutritional authority, with therapists balancing nutritional considerations and psychological safety, influenced by contextual differences between UK and Greek mental health systems. Conclusions: Findings suggest that dietary behavior in ED treatment may benefit from approaches that integrate psychological and nutritional perspectives. Clinicians may consider attending to clients’ emotional readiness, the symbolic meanings of food, and the dynamics of multidisciplinary collaboration, offering insights that can inform clinical practice and future research. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

Background: Real-time and accurate analysis of breast ultrasounds is crucial for diagnosis but remains challenging due to issues like low image contrast and operator dependency. This study aims to address these challenges by developing an integrated framework for real-time lesion detection and tracking. Methods: The proposed system combines Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, a transfer learning-enhanced YOLOv11 model following a continual learning paradigm for cross-center generalization in for lesion detection, and a novel Detection-Based Tracking (DBT) approach that integrates Kernelized Correlation Filters (KCF) with periodic detection verification. The framework was evaluated on a dataset comprising 11,383 static images and 40 ultrasound video sequences, with a subset verified through biopsy and the remainder annotated by two radiologists based on radiological reports. Results: The proposed framework demonstrated high performance across all components. The transfer learning strategy (TL12) significantly improved detection outcomes, achieving a mean Average Precision (mAP) of 0.955, a sensitivity of 0.938, and an F1 score of 0.956. The DBT method (KCF + YOLO) achieved high tracking accuracy, with a success rate of 0.984, an Intersection over Union (IoU) of 0.85, and real-time operation at 54 frames per second (FPS) with a latency of 7.74 ms. The use of CLAHE preprocessing was shown to be a critical factor in improving both detection and tracking stability across diverse imaging conditions. Conclusions: This research presents a robust, fully integrated framework that bridges the gap between speed and accuracy in breast ultrasound analysis. The system’s high performance and real-time efficiency underscore its strong potential for clinical adoption to enhance diagnostic workflows, reduce operator variability, and improve breast cancer assessment. Full article

Capturing kinematic data in outdoor sports is challenging, as motions span large capture volumes and occur under difficult environmental conditions. Video-based approaches, particularly with pan–tilt–zoom cameras, offer a practical solution, but the extensive manual post-processing required limits their use to short sequences and few athletes. This study presents a motion capture pipeline that automates the detection of both reference points and sport-specific keypoints to overcome this limitation. The field test employed eight cameras covering a $250\times 80\times 30 \mathrm{m}$ capture volume with nearly 300 reference points. Ten state-certified ski instructors performed eight standardized maneuvers. Reference points were localized through a hybrid approach combining YOLO object detection and ArUco marker identification. AlphaPose was fine-tuned on a new manually annotated dataset to detect skier-specific keypoints (e.g., skis, poles) alongside anatomical landmarks. Continuous frame-wise calibration and 3D reconstruction were performed using Direct Linear Transformation. Evaluation compared automated detections with manual annotations. Automated reference point detection achieved a mean localization error of $4.1$ pixels ($0.1\%$ of 4K width) and reduced 3D segment-length variation by $23\%$. The skier-specific keypoint model reached $98\%$ PCK, mAP of $0.97$, and an MPJPE of $10.3$ pixels while lowering 3D segment-length variation by $0.5 \mathrm{cm}$ compared to manual digitization and $0.6 \mathrm{cm}$ relative to a pretrained model. Replacing manual digitization with automated detection improves accuracy and facilitates kinematic data collection in large outdoor fields with many athletes and trials. The approach also enables the creation of sport-specific datasets valuable for biomechanical research and training next-generation 3D pose estimation models. Full article

Segmenting non-rigid objects such as smoke in video requires effective utilization of temporal information, which remains challenging due to their irregular deformation and complex appearance variations. Based on our previously proposed DeffNet for industrial fumes video segmentation, this letter presents a novel adaptive frame selection algorithm that employs fuzzy logic control to dynamically optimize the temporal processing step size for the specific task of industrial smoke video segmentation. Our method quantifies inter-frame variation using the Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) as inputs to a fuzzy inference system. Gaussian membership functions, shaped via K-means clustering, and a five-rule fuzzy system are designed to determine the optimal step size, maximizing informative dynamic feature extraction while minimizing redundant computation. As a lightweight front-end module, the algorithm integrates seamlessly into the existing DeffNet segmentation framework without reconstructing new network architecture. Extensive experiments on a dedicated industrial smoke video dataset demonstrate that our approach effectively improves the segmentation performance of DeffNet, achieving 84.27% Intersection over Union (IoU) while maintaining a high inference speed of 39.71 FPS. This work provides an efficient and scene-specific solution for temporal modeling in industrial smoke non-rigid object segmentation and offers a practical improved strategy for DeffNet in real-time industrial smoke monitoring. Full article

The industrial dismantling and recycling of end-of-life photovoltaic (PV) modules require robust visual inspection under dust contamination, inter-class similarity, and constrained edge-computing conditions. This study proposes an end-to-end framework that detects key module components (junction box, backsheet label, aluminum frame, and shadow region) and estimates the aluminum frame gap height for dismantling control. The primary novelty is a dust erosion-aware detection and metrology framework that couples frequency-enhanced visual perception with shadow-guided geometric measurement, while lightweight deployment modules serve as supporting engineering components. Specifically, DWT/FFT-based enhancement with CLAHE is used to improve degraded features, and YOLOv8 is strengthened by GSConv and Coordinate Attention in the backbone and neck; transfer learning, INT8 quantization-aware training, and CMFH-based compact rechecking are further introduced for practical deployment. Experiments show that the proposed method improves mAP@0.5 by 5.08 percentage points over baseline YOLOv8 while increasing speed from 45 to 52 FPS. For geometric metrology, the method achieves 93.0% accuracy with a mean error of 0.45 mm. The results demonstrate an accurate, robust, and edge-deployable solution for the automated inspection and recycling of end-of-life PV modules under dusty conditions. Full article

Background: The early detection of autism spectrum disorder (ASD) is imperative for enhancing long-term developmental outcomes. Nevertheless, conventional screening methods depend on time-consuming, expert-driven behavioral assessments and are characterized by limited scalability. Automated video-based analysis provides a noninvasive and objective approach for the extraction of behavioral biomarkers from naturalistic recordings. Methods: A modular multimodal framework was developed that integrates motion-based video analysis and facial feature extraction for the purpose of ASD versus typically developing (TD) classification. The system is capable of processing RGB videos, skeleton/stickman representations, and motion trajectory streams. A comprehensive set of kinematic features was extracted, encompassing joint trajectories, velocity and acceleration profiles, posture variability, movement smoothness, and bilateral asymmetry. The repetitive stereotypical behaviors exhibited by the subjects were characterized using frequency-domain analysis via FFT within the 0.3–7.0 Hz band. Facial expression features derived from normalized face crops and landmark-based morphological descriptors were integrated as complementary modalities. The feature-level fusion process was executed subsequent to z-score normalization, and the classification procedure was conducted using a Random Forest model with stratified 5-fold cross validation. The implementation of GPU acceleration was instrumental in facilitating near real-time inference. Results: The motion-based ComplexVideos pipeline demonstrated a cross-validated accuracy of 94.2 ± 2.1% with an area under the ROC curve (AUC) of 0.93. Skeleton-based KinectStickman inputs demonstrated moderate performance, with an accuracy range of 60–80%. In contrast, facial-only models exhibited an accuracy of approximately 60%. The integration of multiple modalities through feature fusion has been demonstrated to enhance the robustness of classification algorithms and mitigate the occurrence of false negative outcomes, thereby surpassing the performance of single-modality models. The mean inference time remained below one second per video frame under standard operating conditions. Conclusions: The experimental results demonstrate that the integration of multimodal cues, including motion and facial features, facilitates the development of effective and efficient video-based screening methods for autism spectrum disorder (ASD). The proposed framework is designed to offer a scalable, extensible, and computationally efficient solution that can support early screening in clinical and remote assessment settings. Full article

Background and Objectives: Stroke is a leading cause of long-term disability in older adults, who often face persistent motor, cognitive, and functional challenges. Conventional stroke rehabilitation programs often involve highly repetitive motor tasks, which may reduce patient motivation and contribute to suboptimal adherence over time. Virtual reality (VR) offers an engaging alternative; however, much of the existing research has focused on specialized rehabilitation-oriented VR systems rather than off-the-shelf commercial platforms. This study evaluated older stroke survivors’ acceptance, tolerability, and lived experiences of a short VR-based rehabilitation session using a commercial game on a commercial wearable VR system. Methods: A single-session convergent mixed-methods design was employed. Thirteen community-dwelling older stroke survivors (mean age 79.2 ± 7.1 years; 9 males, 4 female) completed a 15 min VR session using a commercial wearable VR system. The Technology Acceptance Model (TAM) questionnaire and Simulator Sickness Questionnaire (SSQ) assessed acceptance and tolerability, while semi-structured interviews explored lived experiences. Qualitative data were thematically analyzed. Results: Participants reported high acceptance across all TAM domains (overall M = 4.35 ± 0.79, scale 1–5). Enjoyment/intention to use was rated highest (M = 4.77 ± 0.42), while perceived usefulness was lowest (M = 4.15 ± 0.77). VR was well tolerated: the SSQ total score was 17.38 ± 1.73, with most symptoms rated at the mild level only. Exploratory Spearman correlations revealed a significant positive association between age and SSQ total score (r_h = +0.568, p = 0.043). Thematic analysis identified five themes: (1) usability and accessibility; (2) therapeutic value; (3) engagement and motivation; (4) social and clinical support; and (5) physical and cognitive demands. Conclusions: A commercial wearable VR system was found to be acceptable, safe, and engaging for older stroke survivors. With supervision and therapeutic framing, it may serve as a motivating adjunct to conventional rehabilitation. Full article

Transnational youth frequently navigate multiple languages and continually negotiate not only affiliation, but also the legitimacy of the languages they use within changing linguistic hierarchies. However, their educational experiences are often framed through fragmented classroom practices, deficit-based assessments, and nationally bounded curricular frameworks. In this paper, I respond by theorizing Fictional Linguistic Landscapes (FLL) as a transdisciplinary pedagogical approach that utilizes fiction and participatory cultural practices to position language learning as a form of semiotic design, critical inquiry, and identity (re)work. Grounded in linguistic landscape studies, multiliteracies pedagogy, and fan-based meaning-making, FLL positions learners as world-builders and allows them to experiment with visibility, hierarchy, and language(s) in safe fictional environments. This study outlines the four-phase FLL in Second Language Teaching and Learning (L2TL) cycle and provides five pedagogical design spaces to address issues of raciolinguistic valuation, deficit institutional representations, affective harm, peer-level marginalization, and translocal or return migrant identity negotiation. Rather than viewing imagination as an outcome of teaching, FLLinL2TL structures it as a necessary process for learning, linking creative production to explicit linguistic objectives and reflective justification. I conclude by discussing implications for classroom practice, teacher education, and future research on the potential of the FLLinL2TL approach in transnational education research. Full article

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Full article

The effective cleaning of corn prior to storage is crucial for ensuring grain quality and safety. Traditional Convolutional Neural Network (CNN)-based detection methods often struggle to maintain accuracy in scenarios with dense occlusions. Furthermore, limitations in image quality and feature representation hinder their generalization to diverse impurity types. To address these challenges, this paper proposes an enhanced real-time detector transformer model named RT-DETR-CD (Real-Time Detector Transformer with Convolution and Dynamic Upsampling) for corn impurity detection based on industrial vision. This approach integrates Receptive Field Attention Convolutions (RFAConv) to enhance sensitivity to local texture details and employs the dynamic upsampling operator DySample to restore high-frequency edge information. Additionally, a novel Inner-Shape-IoU loss function is introduced to accelerate bounding box regression for objects with varying aspect ratios. Images were captured using FLIR industrial cameras under controllable annular LED illumination. Experiments on a self-built dataset demonstrate that the proposed model achieves a 4.7% improvement in mean average precision (mAP) and operates at 68 frames per second (FPS), outperforming the original RT-DETR model in both accuracy and speed. This work provides a practical solution for real-time, high-precision impurity detection on grain processing lines. Full article

As global demands for nature reserve management intensify, intelligent monitoring has become a pivotal trend. Integrating artificial intelligence with infrared camera traps enables automated analysis of endangered species behavior, providing timely insights for conservation. However, complex habitats often degrade the performance of existing detection technologies. Focusing on the giant panda—a flagship conservation species—we constructed a novel dataset from long-term field monitoring videos and developed an improved PandaSlowFast network. Our model employs channel attention to enhance temporal features, uses small-kernel depth-wise convolutions and dilated convolutions to expand receptive fields for spatial feature extraction, and introduces the Adaptive SwisH activation function to improve adaptability and training stability. The results show that PandaSlowFast achieves 85.38% mean average precision (mAP), outperforming existing methods. An FP16-quantized version maintains comparable accuracy (85.16% mAP) while running at 3.2 frames per second on a Raspberry Pi 4, demonstrating practical deployability for on-site monitoring. This work provides technical support for intelligent panda behavior analysis and offers a transferable methodology for monitoring other rare species, contributing to biodiversity conservation. Full article

Standard compatibility-based truss models, including the Disturbed Stress Field Model (DSFM), often underestimate the shear strength and deformation capacity of reinforced-concrete (RC) beam-column joints. This study investigates the origin of this bias through a systematic inverse identification framework and derives joint-core constitutive relationships tailored to the highly confined, nonuniform stress states of joints. Inverse analyses show that improving confinement effectiveness alone leads to unrealistic parameter saturation and cannot reproduce the measured energy absorption, indicating that conventional compression-softening formulations remain excessively punitive for joint cores. When confinement activation and softening are identified simultaneously, a clear mechanism shift emerges: unlike panel-based theories that link softening to tensile-cracking measures (principal strain ratio), joint softening is overwhelmingly governed by the principal compressive strain, consistent with crushing-dominated damage accumulation. Based on these trends, unified power-law expressions are proposed for both passive confinement activation and damage-induced softening as functions of principal compressive strain only, adhering to a parsimonious formulation without auxiliary variables such as concrete strength or reinforcement ratio ($R^2\approx 0.89$). The model is validated on an independent database of 113 specimens, including high-strength concrete and exterior joints, eliminating the systematic conservatism of the standard DSFM and improving the mean experimental-to-predicted strength ratio from 0.85 to 1.01 while reducing the coefficient of variation from 34.5% to 13%. The proposed formulation supports more reliable joint shear backbone predictions for seismic assessment of RC frame buildings. Full article

Reducing anthropogenic greenhouse gas (GHG) emissions across beef production raises critical questions about consumers’ acceptance and willingness to pay (WTP) for low-carbon beef. As a purely environmental attribute, low-carbon choices are often driven by social and psychological motivations rather than direct personal benefit. This study aims to identify how the social and psychological factors of warm glow feelings, protest beliefs, and social norms influence Chinese urban consumers’ WTP for low-carbon beef. Utilizing survey data from 760 consumers in Beijing, we employed both the double-bounded dichotomous choice contingent valuation method (CVM) and the inferred valuation method (IVM) to assess consumers’ own WTP and inferred WTP for low-carbon beef. The results showed that urban Chinese consumers generally indicated a willingness to pay a premium for low-carbon beef with mean own and inferred WTP values at RMB 47 and RMB 45.29 per 500 g, representing premium rates of 17.49% and 13.23%, respectively. Consumers’ warm glow feelings, protest beliefs, and social norms significantly influenced their own WTP for low-carbon beef, whereas their inferred WTP was mainly affected by social norms. Consumers’ environmental concern had no statistically significant effect on either own WTP or inferred WTP. Policymakers should frame low-carbon beef consumption as a source of personal psychological benefit, mandate transparency regarding the allocation of premium payments of low-carbon beef and establish low-carbon consumption role models within communities. Full article