Search Results (302)

Since the 1970s, accelerated urban development in Doha has contributed to a disjointed and inefficient city structure. While the Doha Metro has begun to address spatial and mobility-related challenges, planners continue to call for a more integrated, strategic approach to ensure safe, accessible, and efficient transit connectivity. In response, the Qatar National Development Framework provides a long-term vision for sustainable urban transformation, with a central aim of embedding the Metro system within the existing urban context and aligning expansion with Transit-Oriented Development (TOD), which promotes dense, multifunctional, pedestrian-oriented neighborhoods along transit corridors. Within this context, this study investigates how TOD strategies can enhance quality of life in mixed-use environments, focusing on the area surrounding Al Mansoura metro station and the adjacent Najma and Al Mansoura districts. Using the Integrated Modification Methodology (IMM), the analysis assesses spatial structure across density, spatial diversity, and connectivity, and derives evidence-based recommendations to improve livability and support sustainable revitalization. To broaden regional applicability, the study also compares Al Mansoura with Olaya in Riyadh—two mid-to-late 20th-century, high-density mixed-use districts undergoing TOD-driven transition—highlighting how spatial form, infrastructure legacy, and urban governance shape TOD outcomes and inform adaptable TOD frameworks for Gulf cities. Full article

Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain–computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity (“black-box” issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes. Full article

Autonomous driving (AD) has emerged as a transformative technology that holds the potential to free humans from the need for manual driving and provide a safer, more comfortable and efficient driving experience. However, most AD systems make decisions solely based on vehicle dynamics and environmental factors such as road conditions and surrounding vehicles, while the occupant’s mental states, such as subjective feelings and experience, are neglected. As a result, autonomous vehicles (AVs) often fail to meet the occupant’s physical and mental demands, ultimately leading to a compromised driving experience. In this study, we propose an occupant-aware decision-making paradigm (ODP) for AD systems. ODP first perceives the occupant’s physical and physiological states that are closely related to mental states, such as facial expressions and physiological signals, through the occupant monitoring system (OMS). Then, a large vision-language model (VLM) processes the occupant’s physical and physiological states via the chain of thought (CoT) technique to analyze the occupant’s mental states and infer the occupant’s needs. Finally, the VLM makes driving decisions that match the occupant’s demands and preferences. Experimental results show that ODP can make decisions that are significantly better aligned with the occupant’s actual needs than existing methods. Full article

Accurate apple detection and precise three-dimensional (3D) localisation are essential for autonomous robotic harvesting in orchard environments, where occlusion, illumination variation, depth noise, and the similar colour appearance of fruits and surrounding leaves present significant challenges. This paper proposes a dual-detector vision framework combined with depth-aware back-projection to achieve robust apple detection and metric 3D localisation in real time. The method integrates the complementary strengths of YOLOv8 and Mask R-CNN through confidence-weighted fusion of bounding boxes and pixel-wise union of segmentation masks, producing stabilised two-dimensional (2D) apple representations under visually ambiguous conditions. The fusion results are converted into dense 3D representations through depth-guided projection within the camera coordinate system representing the visible fruit surface. A depth-consistency weighting strategy assigns higher influence to depth-reliable pixels during centroid computation, thereby suppressing noisy or occluded depth measurements and improving the stability of 3D fruit centre estimation, while local intensity normalisation standardises neighbourhood-level pixel intensities to reduce the impact of shadows, highlights, and uneven lighting, enabling more consistent segmentation and detection across varying illumination conditions. Experimental results demonstrate an accuracy of 98.9%, an mAP of 94.2%, an F1-score of 93.3%, and a recall of 92.8%, while achieving real-time performance at 86.42 FPS, confirming the suitability of the proposed method for robotic harvesting in challenging orchard environments. Full article

Semantic point cloud maps play a pivotal role in smart agriculture. They provide not only core three-dimensional data for orchard management but also empower robots with environmental perception, enabling safer and more efficient navigation and planning. However, traditional point cloud maps primarily model surrounding obstacles from a geometric perspective, failing to capture distinctions and characteristics between individual obstacles. In contrast, semantic maps encompass semantic information and even topological relationships among objects in the environment. Furthermore, existing semantic map construction methods are predominantly vision-based, making them ill-suited to handle rapid lighting changes in agricultural settings that can cause positioning failures. Therefore, this paper proposes a positioning and semantic map reconstruction method tailored for orchards. It integrates visual, LiDAR, and inertial sensors to obtain high-precision pose and point cloud maps. By combining open-vocabulary detection and semantic segmentation models, it projects two-dimensional detected semantic information onto the three-dimensional point cloud, ultimately generating a point cloud map enriched with semantic information. The resulting 2D occupancy grid map is utilized for robotic motion planning. Experimental results demonstrate that on a custom dataset, the proposed method achieves 74.33% mIoU for semantic segmentation accuracy, 12.4% relative error for fruit recall rate, and 0.038803 m mean translation error for localization. The deployed semantic segmentation network Fast-SAM achieves a processing speed of 13.36 ms per frame. These results demonstrate that the proposed method combines high accuracy with real-time performance in semantic map reconstruction. This exploratory work provides theoretical and technical references for future research on more precise localization and more complete semantic mapping, offering broad application prospects and providing key technological support for intelligent agriculture. Full article

Precise medical segmentation of oral ulcers is mandatory and crucial for early diagnosis, but it remains a very challenging task due to rich backgrounds, overexposed or underexposed lesions, and the complex surrounding areas. Therefore, in order to address this challenge, this paper introduces HF-EdgeFormer, a novel hybrid model for oral ulcer segmentation on the AutoOral dataset. This U-shaped transformer-like architecture is, based on publicly available models, the second documented solution for oral ulcer segmentation and it explicitly integrates high-order frequency interactions by using multi-dimensional edge cues. At the encoding stage, a HFConv (High-order Focus Convolution) module divides the feature channels into local streams and global streams, performing learnable filtering via FFT and depth-wise convolutions. After that, it fuses them through stacks of focal transformers and attention gates. In addition to the HFConv block, there are two edge-aware units: the EdgeAware Localization module (that uses eight-direction Sobel filters) and a new Precision EdgeEnhance module (channel-wise Sobel fusion), both used in order to reinforce the boundaries. Skip connections imply Multi-dilated Attention Gates, accompanied by a Spacial-Channel Attention Bridge to accentuate lesion-consistent activations. Moreover, the novel architecture employs an innovative lightweight vision transformer-based bottleneck. It consists of four SegFormerBlock modules localized at the network’s deepest point, so we can achieve global relational modeling exactly where the largest receptive field is present. The model is trained on the AutoOral dataset (introduced by the same team that developed the HF-Unet arhitecture), but due to the limited available images, it needed to be extended by using extensive geometric and photometric augmentations (like RandomAffine, flips, and rotations). This novel architecture achieves a test Dice score of almost 82% and a little over 85% sensitivity while maintaining high precision and specificity, highly valuable in medical segmentation. These results surpass prior HF-UNet baselines while maintaining the model light, with minimal inference memory gains. Full article

This article examines how the Park Chung Hee regime reshaped the public memory of the Neo-Confucian philosopher Yi I (penname Yulgok, 1536–1584) by recasting him as a model of patriotic nationalism. Beginning with the inauguration of the Yulgok Festival in 1962, Yi I was no longer commemorated solely as a scholar of the Chosŏn dynasty; instead, the regime portrayed him as a patriotic sage who advocated for military preparedness. Drawing on archival materials (presidential speeches, heritage management reports, newspaper articles), this study reconstructs the policy discourse surrounding Yulgok and traces the state-driven mechanisms that reframed his public image. The analysis shows that Yulgok’s image became embedded in political rituals, monumentalized in public spaces, circulated in everyday life through currency iconography, and materialized in physical heritage sites transformed to embody a purified, idealized vision of the past. Together, these initiatives positioned the state as the custodian of Yulgok’s memory, aligning his image with the ideological priorities of the militarist state. Full article

This article examines the sacred narrative traditions surrounding Nūr Atā, a small town in present-day Uzbekistan, to explore how Muslim communities in Central Asia expressed their religious history. Drawing on seven manuscripts preserved at the Beruni Institute of Oriental Studies in Tashkent, six in Persian and one in Turkic, the study identifies two distinct traditions that portray the town’s sanctity through prophetic miracle stories, hadith transmission chains, and Sufi cosmology. It explores how narrative form, linguistic variation, and intertextual references shape distinct devotional and historiographical claims. The topics addressed include the relationship between sacred narrative and historiography, the role of ritual practice in sacralizing space, and the textual transmission of spiritual authority. The sacred history of Nūr Atā offers a compelling vision of the town’s religious significance, communicated through both the content and structure of its narratives. These accounts position the town not merely as a local pilgrimage site but as a locus of divine favor embedded within the sacred geography of Islam. By linking the Prophet’s Miʿrāj, angelic testimony, and Sufi initiatic traditions to the landscape of Nūr Atā, the texts construct a genealogy of sanctity that aligns the local with the universal. In doing so, they articulate a vision of communal identity rooted in divine election, prophetic blessing, and spiritual legitimacy. The case of Nūr Atā thus underscores the need to treat sacred narratives, pilgrimage guides, and genealogical traditions as forms of historiography in their own right. These sources do not merely supplement court chronicles or administrative histories; they constitute vital modes through which Central Asian Muslim communities preserved collective memory, asserted religious authority, and inscribed themselves within the broader landscape of the Islamic world. Full article

Camouflaged Object Detection (COD) is a challenging computer vision task aimed at accurately identifying and segmenting objects seamlessly blended into their backgrounds. This task has broad applications across medical image segmentation, defect detection, agricultural image detection, security monitoring, and scientific research. Traditional COD methods often struggle with precise segmentation due to the high similarity between camouflaged objects and their surroundings. In this study, we introduce a Boundary-Guided Differential Attention Network (BDA-Net) to address these challenges. BDA-Net first extracts boundary features by fusing multi-scale image features and applying channel attention. Subsequently, it employs a differential attention mechanism, guided by these boundary features, to highlight camouflaged objects and suppress background information. The weighted features are then progressively fused to generate accurate camouflage object masks. Experimental results on the COD10K, NC4K, and CAMO datasets demonstrate that BDA-Net outperforms most state-of-the-art COD methods, achieving higher accuracy. Here we show that our approach improves detection accuracy by up to 3.6% on key metrics, offering a robust solution for precise camouflaged object segmentation. Full article

The deformation and failure of surrounding rock in underground roadways are governed by complex mechanical interactions and environmental factors, yet the fundamental scientific patterns behind these processes remain unclear. This lack of real-time, data-driven understanding limits the development of intelligent monitoring and prediction systems in mining engineering. To address this challenge, this study aims to establish an intelligent system for the dynamic monitoring and prediction of roadway surrounding rock deformation based on binocular vision and machine learning. An improved Semi-Global Block Matching (SGBM) algorithm is developed for real-time 3D deformation measurement, while a physical similarity model is constructed to visualize the deformation–failure evolution. The Random Forest (RF) algorithm is employed for deep deformation prediction, and its optimal parameters are determined by minimizing the mean square error. Experimental results show that the average measurement errors of the binocular vision method are 1.22 mm and 0.92 mm, outperforming total station monitoring. The gradient-enhanced Random Forest (GERF) model achieves RMSE values of 0.0164 and 0.0113, with R² values of 0.8856 and 0.8356, respectively. Compared with AdaBoost, XGBoost, and Vision Transformer models, GERF improves predictive accuracy by 7.82%, 8.68%, and 3.87%, respectively. These findings demonstrate the scientific feasibility and technical advantage of the proposed intelligent system, offering a new approach to understanding and predicting roadway deformation and failure in intelligent mining. Full article

Accurate semantic segmentation of remote sensing images is crucial for geographical studies. However, mainstream segmentation methods, primarily based on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), often fail to effectively capture edge features, leading to incomplete image feature representation and missing edge information. Moreover, existing approaches generally overlook the modeling of relationships between channel and spatial dimensions, restricting effective interactions and consequently limiting the comprehensiveness and diversity of feature representation. To address these issues, we propose an Edge-Fused Multidimensional Attention Network (EFMANet). Specifically, we employ the Sobel edge detection operator to obtain rich edge information and introduce an Edge Fusion Module (EFM) to fuse the downsampled features of the original and edge-detected images, thereby enhancing the model’s ability to represent edge features and surrounding pixels. Additionally, we propose a Multi-Dimensional Collaborative Fusion Attention (MCFA) Module to effectively model spatial and channel relationships through multi-dimensional feature fusion and integrate global and local information via an attention mechanism. Extensive comparative and ablation experiments on the Vaihingen and Potsdam datasets from the International Society for Photogrammetry and Remote Sensing (ISPRS), as well as the Land Cover Domain Adaptation (LoveDA) dataset, demonstrate that our proposed EFMANet achieves superior performance compared to existing state-of-the-art methods. Full article

Intraocular lens (IOL) implantation is currently the most effective method for restoring vision following cataract surgery and is also used in cases of high myopia or hyperopia. However, IOL implantation eliminates accommodation, forcing patients to choose between corrected distance vision, requiring reading glasses for near tasks, or near vision supplemented by distance correction with spectacles. This limitation underscores the need for fully customized, patient-specific IOLs. To address this challenge, we performed femtosecond laser ablation experiments on polymethyl methacrylate (PMMA) IOLs using 200 fs pulses at 513 nm to investigate controlled surface modification. Laser-induced surface structuring offers a pathway to inscribe micron-scale patterns, including apodized features, in transparent polymers. To our knowledge, this is the first demonstration of femtosecond laser irradiation at 513 nm applied to IOL surfaces. Furthermore, this study is the first to combine scanning electron microscopy (SEM) and optical coherence tomography (OCT) as detection technologies to analyze and quantify ablation morphology and depth. The formation of smooth craters with minimal surrounding thermal damage highlights the potential of femtosecond laser processing as a promising tool for the development of customized, patient-tailored intraocular lenses. Full article

U-Space, the European Union’s legal framework for enabling drone traffic in low altitude, has implications extending beyond airspace management, particularly on the sustainable development of urban areas. This article presents a case study involving regional and local level representatives, examining anticipated concerns and strategic interests, as well as managing requirements in urban U-Space planning. Following a three-stage capacity building process conducted in the German federal state of Hamburg, the results specify ambitions for enhancing economic attractiveness coupled with locally embedded visions for improved public service provision. Instruments that have shown apposite in the given setting to address concerns surrounding public order and security, as well as the impairment of area functions, are presented. The challenges of implementing U-Space in alignment with societal expectations are outlined. Based on the discussion of these findings, recommendations for local-level capacity-building policy and the multi-level governance of U-Space are derived. Full article

Fuchs endothelial corneal dystrophy (FECD) is the leading cause of endothelial failure requiring keratoplasty in industrialised nations. Descemet membrane endothelial keratoplasty (DMEK) has become the gold-standard surgical therapy, yet it is constrained by limited donor tissue and a steep learning curve. This narrative review summarises current and emerging therapeutic strategies for FECD. We describe conventional endothelial keratoplasty and its outcomes, tissue-sparing procedures such as descemetorhexis without endothelial keratoplasty (DWEK) and quarter-DMEK, regenerative approaches including cultured endothelial cell injection and synthetic corneal substitutes, and adjunctive innovations ranging from Rho-associated kinase inhibitors to artificial intelligence-assisted diagnostics. Challenges surrounding donor shortages, variable clinical outcomes, regulatory hurdles and cost are critically appraised. We conclude by outlining future directions that are likely to combine advanced surgical techniques with cell-based and biomaterial solutions to deliver accessible, long-term restoration of vision for patients with FECD. Full article

High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recently, standard-definition maps (SDMaps) derived from remote sensing have been adopted as priors to support HDMap perception, lowering cost but struggling with subtle urban changes and localization drift. We propose Loop-MapNet, a self-evolving, multimodal, closed-loop mapping framework. Loop-MapNet effectively leverages surround-view images, LiDAR point clouds, and SDMaps; it fuses multi-scale vision via a weighted BiFPN, and couples PointPillars BEV and SDMap topology encoders for cross-modal sensing. A Transformer-based bidirectional adaptive cross-attention aligns SDMap with online perception, enabling robust fusion under heterogeneity. We further introduce a confidence-guided masked autoencoder (CG-MAE) that leverages confidence and probabilistic distillation to both capture implicit SDMap priors and enhance the detailed representation of low-confidence HDMap regions. With spatiotemporal consistency checks, Loop-MapNet incrementally updates SDMaps to form a perception–mapping–update loop, compensating remote-sensing latency and enabling online map optimization. On nuScenes, within 120 m, Loop-MapNet attains 61.05% mIoU, surpassing the best baseline by 0.77%. Under extreme localization errors, it maintains 60.46% mIoU, improving robustness by 2.77%; CG-MAE pre-training raises accuracy in low-confidence regions by 1.72%. These results demonstrate advantages in fusion and robustness, moving beyond one-way prior injection and enabling HDMap–SDMap co-evolution for closed-loop autonomy and rapid SDMap refresh from remote sensing. Full article