Search Results (2,750)

Background/Objective: The quality of care afforded to rural, remote, and First Nations Peoples is dependent on access to a health workforce with the capacity to contextualize healthcare and practice to the needs and expectations of these populations. In Australia, the lack of representation of rural health in undergraduate and post graduate health professional education undermines this preparedness and consideration of rural practice uptake and longevity, compounding the inequities confronted by 7 million Australians residing in these locations. Urgent educational reforms are required to address this omission, the deficit discourses used to characterize rural healthcare, and the persistent health workforce shortages experienced. This paper presents the Anabranch Framework for the Ruralization of Health Professional Education, a high-level strategy to transform rural healthcare provision, professional practice, and health workforce outcomes. Methods: The framework was developed through an iterative process involving a series of systematic steps. The process included the following: individual and group critical dialogues with internal academic educators, external health service leaders, metropolitan academic allies, and leaders of other rural health academic departments; an internal review of empirical studies of relevance to the ruralization of health professional education and practice; the visualization of a place-based framework; the academic conceptualization of the framework; and further critical dialogues to test the framework’s face validity. Results: The Anabranch Framework comprises four inter-related rural domains: theories, pedagogies, practices, and connectivity; four constructs: knowledge acquisition and generation, immersion in rural curriculum, knowledge translation and sharing, and relational practice; and two structural elements: spiraled and scaffolded curriculum and duration and the quality of rural placement and practice. Conclusions: The Anabranch Framework is a high-level strategy to ruralize health professional worldviews, advance rural person-centered practice, enable a deeper understanding of rural places and the development of an equity-orientated, sustainable and rural-literate health workforce. Full article

Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context. Full article

Hyperspectral anomaly detection (HAD) aims to identify pixels that significantly differ from the background without prior knowledge. While deep learning-based reconstruction methods have shown promise, they often suffer from limited feature representation, inefficient training cycles, and sensitivity to imbalanced data distributions. To address these challenges, this paper proposes a novel contrastive–transfer-synergized dual-stream transformer for hyperspectral anomaly detection (CTDST-HAD). The framework integrates contrastive learning and transfer learning within a dual-stream architecture, comprising a spatial stream and a spectral stream, which are pre-trained separately and synergistically fine-tuned. Specifically, the spatial stream leverages general visual and hyperspectral-view datasets with adaptive elastic weight consolidation (EWC) to mitigate catastrophic forgetting. The spectral stream employs a variational autoencoder (VAE) enhanced with the RossThick–LiSparseR (R-L) physical-kernel-driven model for spectrally realistic data augmentation. During fine-tuning, spatial and spectral features are fused for pixel-level anomaly detection, with focal loss addressing class imbalance. Extensive experiments on nine real hyperspectral datasets demonstrate that CTDST-HAD outperforms state-of-the-art methods in detection accuracy and efficiency, particularly in complex backgrounds, while maintaining competitive inference speed. Full article

The Case Management Model and Notation (CMMN) supports the modeling of dynamic, semi-structured, and knowledge-intensive processes, but its adoption remains limited due to conceptual and visual shortcomings. Using a Design Science Research Method (DSRM), this study introduces a notational variant of CMMN, termed CMMN+, comprising three structural and visual enhancements: explicit representation of activation logic, enriched data entity modeling through semantically grounded metadata and structured role assignments based on the RACI framework. Cognitive effectiveness is analytically evaluated using the nine principles of the Physics of Notations (PoN). The analysis demonstrates clear improvements in semiotic clarity, semantic transparency and perceptual discriminability, confirming enhanced interpretability of the proposed notational variant. As expected, trade-offs arise with respect to graphic economy, while principles such as cognitive fit require subsequent empirical validation. CMMN+ constitutes a conceptually and technically grounded notational advancement in case management modeling by systematically aligning language design with cognitive-effectiveness theory. The presented results establish a strong foundation for integrating more intuitive and semantically rich modeling support into practice. Full article

Industrial visual anomaly detection remains challenging because practical inspection systems must achieve high detection accuracy while operating under highly imbalanced data, diverse defect patterns, limited computational resources, and increasing demands for interpretability. This work aims to develop a lightweight yet effective and explainable anomaly detection framework for industrial images in settings where a limited number of labeled anomalous samples are available. We propose a dual-branch feature-based supervised ensemble method that integrates complementary representations: a PCA branch to capture linear global structure and a scattering branch to model multi-scale textures. A heterogeneous pool of classical learners (SVM, RF, ET, XGBoost, and LightGBM) is trained on each feature branch, and stable probability outputs are obtained via stratified K-fold out-of-fold training, probability calibration, and a quantile-based threshold search. Decision-level fusion is then performed by stacking, where logistic regression, XGBoost, and LightGBM serve as meta-learners over the out-of-fold probabilities of the selected top-K base learners. Experiments on two public benchmarks (MVTec AD and BTAD) show that the proposed method substantially improves the best PCA-based single model, achieving relative F1_score gains of approximately 31% (MVTec AD) and 26% (BTAD), with maximum AUC values of about 0.91 and 0.96, respectively, under comparable inference complexity. Overall, the results demonstrate that combining high-quality handcrafted features with supervised ensemble fusion provides a practical and interpretable alternative/complement to heavier deep models for resource-constrained industrial anomaly detection, and future work will explore more category-adaptive decision strategies to further enhance robustness on challenging classes. Full article

Time series similarity measurement is a fundamental task underpinning clustering, classification, and anomaly detection. Traditional approaches predominantly rely on one-dimensional data representations, which often fail to capture complex structural dependencies. To address this limitation, this paper proposes a novel similarity measurement framework based on two-dimensional image enhancement. The method initially transforms one-dimensional time series into recurrence plots (RPs), converting temporal dynamics into visually symmetric textures, enhancing the temporal information of the one-dimensional time series. To overcome the potential blurring of fine-grained information during transformation, multi-scale detail boosting (MSDB) is introduced to amplify the high-frequency components and textural details of the RP images. Subsequently, a pre-trained ResNet-18 network is utilized to extract deep visual features from the enhanced images, and the similarity is quantified using the Euclidean distance of these feature vectors. Extensive experiments on the UCR Time Series Classification Archive demonstrate that the proposed method effectively leverages image enhancement to reveal latent temporal patterns. This approach leverages the inherent symmetry properties embedded in recurrence plots. By enhancing the texture of these symmetrical structures, the proposed method provides a more robust and informative basis for similarity assessment. Full article

As one of the most representative cultural heritages, traditional Chinese opera is characterized by highly refined symbolic contexts and stylized narrative structures. Nevertheless, the contemporary generation often struggles with its abstract expression and language, leading to declining attendance. In addition, urbanization and digital entertainment have squeezed out its living spaces, increasing demand for more diverse experiences. To address these issues, this study conducts a systematic and thematically categorized review of the literature, exploring how extended reality (XR) reshapes the spatial and experiential representation of opera culture. Drawing upon the reality–virtuality continuum and spatial computing as theoretical foundations, the research investigates the features, workflows, and cultural adaptability of augmented reality (AR), virtual reality (VR), and mixed reality (MR), identifying how each modality of XR supports distinct modes of space generation and audience engagement. Through comparative analysis, we propose three XR-based approaches for reinterpreting Chinese opera: AR for theatrical spaces visualization, VR for performative narratives embodiment, and MR for opera cultural elements superposition. Overall, the research clarifies that XR can be used as a comprehensive medium to enhance replicability and user perception, contributing to the preservation and communication of humanity’s traditional culture. Full article

Navigation line extraction is essential for visual navigation in agricultural machinery, yet existing methods often perform poorly in complex environments due to challenges such as weed interference, broken crop rows, and leaf adhesion. To enhance the accuracy and robustness of crop row centerline identification, this study proposes an improved segmentation model based on SegNeXt with integrated adaptive region of interest (ROI) extraction for multi-growth-stage maize row perception. Improvements include constructing a Local module via pooling layers to refine contour features of seedling rows and enhance complementary information across feature maps. A multi-scale fusion attention (MFA) is also designed for adaptive weighted fusion during decoding, improving detail representation and generalization. Additionally, Focal Loss is introduced to mitigate background dominance and strengthen learning from sparse positive samples. An adaptive ROI extraction method was also developed to dynamically focus on navigable regions, thereby improving efficiency and localization accuracy. The outcomes revealed that the proposed model achieves a segmentation accuracy of 95.13% and an IoU of 93.86%. The experimental results show that the proposed algorithm achieves a processing speed of 27 frames per second (fps) on GPU and 16.8 fps on an embedded Jetson TX2 platform. This performance meets the real-time requirements for agricultural machinery operations. This study offers an efficient and reliable perception solution for vision-based navigation in maize fields. Full article

Background: Depression is a common mental disorder, and early and objective diagnosis of depression is challenging. New advances in deep learning show promise for processing audio and video content when screening for depression. Nevertheless, the majority of current methods rely on raw video processing or multimodal pipelines, which are computationally costly and challenging to understand and create privacy issues, restricting their use in actual clinical settings. Methods: Based solely on spatiotemporal 3D face landmark representations, we describe a unique, totally visual, and lightweight deep learning approach to overcome these constraints. In this paper we introduce, for the first time, a pure visual deep learning framework, based on spatiotemporal 3D facial landmarks extracted from clinical interview videos contained in the DAIC-WOZ and Extended DAIC-WOZ (E-DAIC) datasets. Our method does not use raw video or any type of semi-automated multimodal fusion. Whereas raw video streaming can be computationally expensive and is not well suited to investigating specific variables, we first take a temporal series of 3D landmarks, convert them to pseudo-images (224 × 224 × 3), and then use them within a CNN-LSTM framework. Importantly, CNN-LSTM provides the ability to analyze the spatial configuration and temporal dimensions of facial behavior. Results: The experimental results indicate macro-average F1 scores of 0.74 on DAIC-WOZ and 0.762 on E-DAIC, demonstrating robust performance under heavy class imbalances, with a variability of ±0.03 across folds. Conclusion: These results indicate that landmark-based spatiotemporal modeling represents the future of lightweight, interpretable, and scalable automatic depression detection. Second, our results suggest exciting opportunities for completely embedding ADI systems within the framework of real-world MHA. Full article

In practical underwater object detection tasks, imbalanced sample distribution and the scarcity of samples for certain classes often lead to insufficient model training and limited generalization capability. To address these challenges, this paper proposes FS2-DETR (Few-Shot Detection Transformer for Sonar Images), a transformer-based few-shot object detection network tailored for sonar imagery. Considering that sonar images generally contain weak, small, and blurred object features, and that data scarcity in some classes can hinder effective feature learning, the proposed FS2-DETR introduces the following improvements over the baseline DETR model. (1) Feature Enhancement Compensation Mechanism: A decoder-prediction-guided feature resampling module (DPGFRM) is designed to process the multi-scale features and subsequently enhance the memory representations, thereby strengthening the exploitation of key features and improving detection performance for weak and small objects. (2) Visual Prompt Enhancement Mechanism: Discriminative visual prompts are generated to jointly enhance object queries and memory, thereby highlighting distinctive image features and enabling more effective feature capture for few-shot objects. (3) Multi-Stage Training Strategy: Adopting a progressive training strategy to strengthen the learning of class-specific layers, effectively mitigating misclassification in few-shot scenarios and enhancing overall detection accuracy. Extensive experiments conducted on the improved UATD sonar image dataset demonstrate that the proposed FS2-DETR achieves superior detection accuracy and robustness under few-shot conditions, outperforming existing state-of-the-art detection algorithms. Full article

Yunnan Jiama (paper horse prints), a representative form of intangible cultural heritage in southwest China, is characterized by subtle inter-class differences, complex woodblock textures, and heterogeneous preservation conditions, which collectively pose significant challenges for digital preservation and automatic image classification. To address these challenges and improve the computational analysis of Jiama images, this study proposes an enhanced object detection framework based on YOLOv8 integrated with a Global Attention Mechanism (GAM), referred to as YOLOv8-GAM. In the proposed framework, the GAM module is embedded into the high-level semantic feature extraction and multi-scale feature fusion stages of YOLOv8, thereby strengthening global channel–spatial interactions and improving the representation of discriminative cultural visual features. In addition, image augmentation strategies, including brightness adjustment, salt-and-pepper noise, and Gaussian noise, are employed to simulate real-world image acquisition and degradation conditions, which enhances the robustness of the model. Experiments conducted on a manually annotated Yunnan Jiama image dataset demonstrate that the proposed model achieves a mean average precision (mAP) of 96.5% at an IoU threshold of 0.5 and 82.13% under the mAP@0.5:0.95 metric, with an F1-score of 94.0%, outperforming the baseline YOLOv8 model. These results indicate that incorporating global attention mechanisms into object detection networks can effectively enhance fine-grained classification performance for traditional folk print images, thereby providing a practical and scalable technical solution for the digital preservation and computational analysis of intangible cultural heritage. Full article

Self-supervised contrastive learning has demonstrated remarkable effectiveness in learning visual representations without labeled data, yet its application to 3D local feature learning from point clouds remains underexplored. Existing methods predominantly focus on complete object shapes, neglecting the critical challenge of recognizing partial observations commonly encountered in real-world 3D perception. We propose a momentum contrastive learning framework specifically designed to learn discriminative local features from randomly sampled point cloud regions. By adapting the MoCo architecture with PointNet++ as the feature backbone, our method treats local parts of point cloud as fundamental contrastive learning units, combined with carefully designed augmentation strategies including random dropout and translation. Experiments on ShapeNet demonstrate that our approach effectively learns transferable local features and the empirical observation that approximately 30% object local part represents a practical threshold for effective learning when simulating real-world occlusion scenarios, and achieves comparable downstream classification accuracy while reducing training time by 16%. Full article

The variability of renewable energy sources, coupled with the decentralised configuration of distributed generation (DG), significantly complicates grid management, necessitating sophisticated visual analytics to enhance power system performance and energy distribution. This paper presents a probabilistic visualisation technique based on polar histograms to identify the dynamic influence zones of DG units by analysing line current flows. The proposed framework explicitly accounts for the probabilistic representation of reverse power flows, which provides an overall view of DG impacts on distribution networks. Quasi-dynamic simulations are conducted on a 33-bus distribution system using DIgSILENT PowerFactory 2020, MATLAB R2020, and Python 3.8. The results demonstrate that the polar histogram approach provides intuitive insights into DG influence, revealing zones of grid-dominated, DG-dominated, and shared interactions. These findings act as a potential practical tool for voltage management, demand balancing, and secure integration of renewable DG units into modern power grids. Full article

All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for robust all-sky scene classification using hemispherical imagery acquired at the Thai National Observatory. The proposed architecture integrates Squeeze-and-Excitation (SE) blocks for radiometric channel stabilization, the Convolutional Block Attention Module (CBAM) for spatial–semantic refinement, and Spatial Pyramid Pooling (SPP) for hemispherical multi-scale context aggregation within a fully fine-tuned EfficientNetB7 backbone, forming a domain-aware atmospheric representation framework. A large-scale dataset comprising 122,660 RGB images across 13 day–night sky-scene categories was curated, capturing diverse tropical atmospheric conditions including humidity, haze, illumination transitions, and sensor noise. Extensive experimental evaluations demonstrate that the EASMNet achieves 93% overall accuracy, outperforming representative convolutional (VGG16, ResNet50, DenseNet121) and transformer-based architectures (Swin Transformer, Vision Transformer). Ablation analyses confirm the complementary contributions of hierarchical attention and multi-scale aggregation, while class-wise evaluation yields F1-scores exceeding 0.95 for visually distinctive categories such as Day Humid, Night Clear Sky, and Night Noise. Residual errors are primarily confined to physically transitional and low-contrast atmospheric regimes. These results validate the EASMNet as a reliable, interpretable, and computationally feasible framework for real-time observatory dome automation, astronomical scheduling, and continuous atmospheric monitoring, and provide a scalable foundation for autonomous sky-observation systems deployable across diverse climatic regions. Full article

The high costs and time-consuming nature of space exploration missions are among the major barriers to studying deep space. The lack of samples and limited information make such studies challenging, highlighting the need for innovative solutions, including advanced data-mining techniques and tools such as geochemical modeling, as strategies for overcoming challenges in data scarcity. Geochemical modeling is a powerful tool for understanding the processes that govern the composition and distribution of elements and compounds in a system. In cosmology, space geochemical modeling could support cosmochemistry by simulating the evolution of the atmospheres, crusts, and interiors of astronomical objects and predicting the geochemical conditions of their surfaces or subsurfaces. This study uniquely focuses on the geochemical modeling of celestial bodies beyond Mars, fills a significant gap in the literature, and provides a vision of what has been done by analyzing, categorizing, and providing the critical points of these research objectives, exploring geochemical modeling aspects, and outcomes. To systematically trace the intellectual structure of this field, this study follows the PRISMA guidelines for systematic reviews. It includes a structured screening process that uses bibliographic methods to identify relevant studies. To this end, we developed the Custom Bibliometric Analyses Toolkit (CBAT), which includes modules for keyword extraction, targeted thematic mapping, and visual network representation. This toolkit enables the precise identification and analysis of relevant studies, providing a robust methodological framework for future research. Europa, Titan, and Enceladus are among the most studied celestial bodies, with spectrometry and thermodynamic models as the most prevalent methods, supported by tools such as FREZCHEM, PHREEQC, and CHNOSZ. By exploring geochemical modeling solutions, our systematic review serves to inform future exploration of distant celestial bodies and assist in ambitious questions such as habitability and the potential for extraterrestrial life in the outer solar system. Full article