Search Results (306)

Digital dentistry is undergoing rapid evolution, with three-dimensional imaging technologies increasingly integrated into routine clinical workflows. Originally developed for accurate dental arch reconstruction, modern intraoral scanners have demonstrated expanding versatility in capturing intraoral mucosal as well as perioral cutaneous structures. Concurrently, photogrammetry has emerged as a powerful method for full-face digital reconstruction, particularly valuable in orthodontic and prosthodontic treatment planning. These advances offer promising applications in forensic sciences, where high-resolution, three-dimensional documentation of anatomical details such as palatal rugae, lip prints, and bite marks can provide objective and enduring records for legal and investigative purposes. This study explores the forensic potential of two digital acquisition techniques by presenting two cadaveric cases of animal bite injuries. In the first case, an intraoral scanner (Dexis 3600) was used in an unconventional extraoral application to directly scan skin lesions. In the second case, photogrammetry was employed using a digital single-lens reflex (DSLR) camera and Agisoft Metashape, with standardized lighting and metric scale references to generate accurate 3D models. Both methods produced analyzable digital reconstructions suitable for forensic archiving. The intraoral scanner yielded dimensionally accurate models, with strong agreement with manual measurements, though limited by difficulties in capturing complex surface morphology. Photogrammetry, meanwhile, allowed for broader contextual reconstruction with high texture fidelity, albeit requiring more extensive processing and scale calibration. A notable advantage common to both techniques is the avoidance of physical contact and impression materials, which can compress and distort soft tissues, an especially relevant concern when documenting transient evidence like bite marks. These results suggest that both technologies, despite their different origins and operational workflows, can contribute meaningfully to forensic documentation of bite-related injuries. While constrained by the exploratory nature and small sample size of this study, the findings support the viability of digitized, non-destructive evidence preservation. Future perspectives may include the integration of artificial intelligence to assist with morphological matching and the establishment of digital forensic databases for pattern comparison and expert review. Full article

High-entropy alloy (HEA) catalysts have attracted significant attention from researchers. In many cases, HEAs exhibit high activity and selectivity for catalytic reactions due to four “core effects”: high entropy effect, lattice distortion effect, slow diffusion effect, and mixing effect. However, a systematic summary of HEA catalyst design and understanding is lacking. In this review, the reasons for the outstanding performance of HEA catalysts are first discussed from multiple perspectives, such as excellent mechanical properties, ultra-high-performance stability, and the potential for compositional optimization. Furthermore, to deepen our understanding of HEA catalysts, the rational design of HEA catalysts is introduced, covering design principles, element selection, and the use of algorithms for prediction. Next, several common preparation methods for HEAs are introduced, including chemical co-reduction, solution combustion, mechanical alloying, and sol–gel methods. Finally, the research progress of HEA catalysts in hydrogen evolution reactions, oxygen evolution reactions, and oxygen reduction reactions is presented. Unlike existing reviews, this work establishes a unified framework connecting HEA fundamentals (entropy effects), computational design, scalable synthesis, and application-specific performance, while identifying underexplored pathways like lattice-oxygen-mediated mechanisms (LOM) for future research. Full article

The integration of the digital and real economies (DRI) is an inevitable trend in future economic growth. This study measures DRI levels across 30 Chinese provinces from 2012 to 2022 using a coupling coordination model with panel data and empirically examines DRI’s impact on industrial structure upgrading (ISU) through fixed-effects models, mediation effect models, and panel threshold models. The findings reveal that (1) DRI promotes industrial structure upgrading, a conclusion that remains valid under robustness tests and endogeneity tests; (2) DRI can facilitate ISU by enhancing consumption levels, correcting factor distortions, and accelerating the marketization process; (3) there exists a threshold effect, with a positive effect of DRI on ISU based on the level of digital economy and the scale of the real economy as threshold variables; (4) the impact of DRI on ISU differs across different regions due to differences in policy support and resource allocation; (5) ISU has a significant spatial spillover effect, as shown by spatial econometric analysis. These conclusions offer a new perspective, practical policy implications for China’s high-quality economic development, and strategic insights to enhance industrial competitiveness in the global value chain. Full article

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in AP, AP₅₀, and AP₇₅, respectively, outperforming state-of-the-art methods while maintaining efficiency. Full article

The construction environment is a highly dynamic and complex system, presenting challenges for accurately identifying and managing dynamic resources in digital twin-based scenes. This study aims to address the problem of object coordinate distortion caused by camera image deformation, which often reduces the fidelity of dynamic object mapping in digital construction monitoring. A novel dynamic object mapping generation method is proposed to enhance precision and synchronization of dynamic objects within a digital twin environment. The approach integrates internal and external camera parameters, including spatial position, field of view (FOV), and camera pose, into BIM using Dynamo, thereby creating a virtual camera aligned with the physical one. The YOLOv11 algorithm is employed to recognize dynamic objects in real-time camera footage, and corresponding object families are generated in the BIM model. Using perspective projection combined with a linear regression model, the system computes and updates accurate coordinate positions of the dynamic objects, which are then fed back into the camera view to achieve real-time mapping. Experimental validation demonstrates that the proposed method significantly reduces mapping errors induced by lens distortion and provides accurate spatial data, supporting improved dynamic resource perception and intelligent management in digital twin construction environments. Full article

On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psychological intervention. In this paper, we introduce two novel datasets from Chinese social media: SOS-HL-1K for suicidal risk classification, which contains 1249 posts, and SocialCD-3K, a multi-label classification dataset for cognitive distortion detection that contains 3407 posts. We conduct a comprehensive evaluation using two supervised learning methods and eight large language models (LLMs) on the proposed datasets. From the prompt engineering perspective, we experiment with two types of prompt strategies, including four zero-shot and five few-shot strategies. We also evaluate the performance of the LLMs after fine-tuning on the proposed tasks. Experimental results show a significant performance gap between prompted LLMs and supervised learning. Our best supervised model achieves strong results, with an F1-score of 82.76% for the high-risk class in the suicide task and a micro-averaged F1-score of 76.10% for the cognitive distortion task. Without fine-tuning, the best-performing LLM lags by 6.95 percentage points in the suicide task and a more pronounced 31.53 points in the cognitive distortion task. Fine-tuning substantially narrows this performance gap to 4.31% and 3.14% for the respective tasks. While this research highlights the potential of LLMs in psychological contexts, it also shows that supervised learning remains necessary for more challenging tasks. Full article

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ($dq$) and stationary ($\alpha \beta$) reference frames, offering a broad perspective on system behavior under various operating conditions. Additionally, an in-depth analysis of the PR controller is presented, highlighting its potential to regulate both positive- and negative-sequence components. This enables the development of more effective and robust tuning methodologies for steady-state and dynamic scenarios. The evaluation is conducted under three main conditions: steady-state operation, transient response to input power variations, and robustness analysis in the presence of grid parameter changes. The study examines the impact of each controller on the total harmonic distortion (THD) of the injected current, as well as on system stability margins and dynamic performance. Practical aspects that are often overlooked are also addressed, such as the modeling of the inverter and photovoltaic generator, the implementation of space vector pulse-width modulation (SVPWM), and the influence of the output LC filter capacitor. The control structures under analysis are validated through numerical simulations performed in MatLab^® software (R2021b) using dedicated computational routines, enabling the identification of strategies that enhance performance and ensure compliance of grid-connected photovoltaic systems. Full article

Understanding the dynamics and drivers of groundwater storage (GWS) is crucial for sustainable resource management. Most studies attribute GWS changes to climate change or human activities, often neglecting external hydrological influences. In this study, we categorize the driving factors influencing GWS changes into three groups: climate change, human activity, and regional hydrological pressure. We emphasize that the coupling effects and potential disturbances from adjacent hydrological systems may significantly affect local groundwater evolution. This perspective differs from conventional approaches that focus solely on local factors. This study analyzes the spatiotemporal evolution of GWS in Shanxi Province, located in the eastern Loess Plateau, from 2003 to 2023 using GRACE and GLDAS data. We examine the linkage between GWS in Shanxi and the North China Plain through correlation analysis, Engle–Granger cointegration tests, and Granger causality tests. The results show that GWS in Shanxi showed an average annual reduction of −17.27 ± 1.4 mm/yr, with the most severe depletion occurring in the southeastern region, which is geographically adjacent to the North China Plain. The results of the Engle–Granger cointegration test and Granger causality analysis reveal a bidirectional causal relationship between GWS changes in the two regions, indicating that changes in GWS in either region may have a significant impact on the other. The results of the contribution analysis indicate that the North China Plain’s groundwater decline contributes approximately −53.89% to the reduction of GWS in Shanxi, while human activities and external hydrological influences together explain over 98% of the change. This result suggests that relying solely on climatic and human activity factors to explain groundwater changes may lead to significant biases, as ignoring interregional hydrological linkages can amplify or obscure the attribution of local groundwater variations, resulting in distorted conclusions. These findings highlight the value of remote sensing in capturing regional hydrological interactions and underscore the need to integrate interregional groundwater connectivity into policy design for sustainable groundwater governance. Full article

As part of contemporary urban culture, murals support place making and city identity. While much attention has been paid to their role in activating public space during daylight hours, their presence after dark remains largely unexamined. This paper analyzes how mural content interacts with night-time illumination. The research draws on case studies, photographs, luminance measurements, and lighting simulations. It evaluates how existing lighting systems support or undermine the legibility and impact of commercial murals in urban environments. It explores whether standardized architectural lighting guidelines suit murals, how color and surface affect visibility, and which practices improve night-time legibility. The study identifies a gap in existing lighting strategies, noting that uneven lighting distorts intent and reduces public engagement. In response, a new design tool—the Floodlighting Content Readability Map—is proposed to support artists and planners in creating night-visible murals. This paper situates mural illumination within broader debates on creative urbanism and argues that lighting is not just infrastructure, but a cultural and aesthetic tool that extends the reach and resonance of public art in the 24 h city. It further emphasizes the need for interdisciplinary collaboration and a multi-contextual perspective—encompassing visual, social, environmental, and regulatory dimensions—when designing murals in cities. Full article

Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims’ faces to bypass authentication mechanisms. The increasing availability of personal images on social media further amplifies this risk, making robust anti-spoofing mechanisms essential for secure facial authentication. To address these challenges, we introduce FaceCloseup, a novel liveness detection technique that strengthens facial authentication by leveraging perspective distortion inherent in close-up shots of real, 3D faces. Instead of relying on additional sensors or user-interactive gestures, FaceCloseup passively analyzes facial distortions in video frames captured by a mobile device’s camera, improving security without compromising user experience. FaceCloseup effectively distinguishes live faces from spoofed attacks by identifying perspective-based distortions across different facial regions. The system achieves a 99.48% accuracy in detecting common spoofing methods—including photo, video, and 3D model-based attacks—and demonstrates 98.44% accuracy in differentiating between individual users. By operating entirely on-device, FaceCloseup eliminates the need for cloud-based processing, reducing privacy concerns and potential latency in authentication. Its reliance on natural device movement ensures a seamless authentication experience while maintaining robust security. Full article

Urban color represents the visual skin of a city, embodying regional culture, historical memory, and the contemporary spirit. However, while the existing studies focus on pedestrian-level facade colors, the “fifth facade” from a bird’s-eye view has been largely overlooked. Moreover, color distortions in traditional remote sensing imagery hinder precise analysis. This study targeted 56 Chinese coastal cities, decoding the spatiotemporal patterns of their fifth facade color (FFC). Through developing an innovative natural color optimization algorithm, the oversaturation and color bias of Sentinel-2 imageries were addressed. Several color indicators, including dominant colors, hue–saturation–value, color richness, and color harmony, were developed to analyze the spatial variations of FFC. Results revealed that FFC in Chinese coastal cities is dominated by gray, black, and brown, reflecting the commonality of cement jungles. Among them, northern warm grays exude solidity, as in Weifang, while southern cool grays convey modern elegance, as in Shenzhen. Blue PVC rooftops (e.g., Tianjin) and red-brick villages (e.g., Quanzhou) serve as symbols of industrial function and cultural heritage. Economically advanced cities (e.g., Shanghai) lead in color richness, linking vitality to visual diversity, while high-harmony cities (e.g., Lianyungang) foster livability through coordinated colors. The study also warns of color pollution risks. Cities like Qingdao exposed planning imbalances through color clashes. This research pioneers a systematic and large-scale decoding of urban fifth facade color from a remote sensing perspective, quantitatively revealing the dilemma of “identical cities” in modernization development. The findings inject color rationality into urban planning and create readable and warm city images. Full article

Layered transition metal oxide (LTMO) cathode materials for sodium-ion batteries (SIBs) have attracted extensive attention due to their unique structural stability and excellent electrochemical performance. However, their poor stability in air has significantly impeded their practical application, as exposure to moisture and carbon dioxide can lead to Na⁺ loss, phase transitions, and decreased electrochemical performance. This paper reviews the application of high-entropy strategies in sodium-ion LTMO cathode materials, focusing on the optimization of air stability and electrochemical performance through approaches including high-entropy cation regulation, P2/O3 dual-phase synergistic structures, and fluorine ion doping. Studies have shown that high-entropy design can effectively inhibit phase transitions, alleviate Jahn–Teller distortion, enhance oxygen framework stability, and markedly enhance the cycle life and rate performance of materials. Furthermore, future research directions are proposed, including the use of advanced characterization techniques to reveal failure mechanisms, the integration of machine learning to optimize material design, and the development of high-performance mixed-phase structures. High-entropy strategies provide new perspectives for the development of SIBs cathode materials with enhanced air stability, potentially promoting the practical application of SIBs in large-scale energy storage systems. Full article

Financial support for agriculture has mainly focused on grain production, while insufficient efforts have been made to ensure water security, potentially intensifying water pressure in grain production (WPGP). This study applies the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to measure WPGP from the perspective of sustainable agricultural water use, investigating the impact of agricultural fiscal expenditure on WPGP. Our findings reveal several key points. First, there is a clear linkage between the spatial and temporal patterns of fiscal support and WPGP. Projections indicate that water pressure for grain production in China will continue to rise from 2019 to 2030, with the fastest increases in the Northeast and Huang-Huai-Hai regions, at 20.53% and 13.39%, respectively. Second, agricultural fiscal expenditure distorts the allocation of grain production factors, causing cultivation areas to expand beyond local water resource capacity and, thus, exacerbating WPGP. This effect exhibits a time lag due to the gradual nature of factor allocation. Further analysis shows that in non-major grain-producing regions, lower production efficiency and higher opportunity costs of factor use weaken the impact of fiscal expenditure on WPGP compared to major grain-producing regions. Third, in regions with advanced technical conditions for grain production, the negative impact of agricultural fiscal expenditure on WPGP is mitigated by higher irrigation technology levels, improved water allocation efficiency, and lower water demand per unit of grain. Fourth, the public good characteristics of water resources and water conservancy facilities—namely, strong externalities and non-exclusivity—along with the agronomic demonstration effect, lead to a spatial spillover effect of agricultural fiscal expenditure on WPGP. Full article

While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction. Full article

Pansharpening provides a computational solution to the resolution limitations of imaging hardware by enhancing the spatial quality of low-resolution hyperspectral (LRMS) images using high-resolution panchromatic (PAN) guidance. From an information-theoretic perspective, the task involves maximizing the mutual information between PAN and LRMS inputs while minimizing spectral distortion and redundancy in the fused output. However, traditional spatial-domain methods often fail to preserve high-frequency texture details, leading to entropy degradation in the resulting images. On the other hand, frequency-based approaches struggle to effectively integrate spatial and spectral cues, often neglecting the underlying information content distributions across domains. To address these shortcomings, we introduce a novel architecture, termed the Cross-Domain Fusion Attention Network (CDFAN), specifically designed for the pansharpening task. CDFAN is composed of two core modules: the Multi-Domain Interactive Attention (MDIA) module and the Spatial Multi-Scale Enhancement (SMCE) module. The MDIA module utilizes discrete wavelet transform (DWT) to decompose the PAN image into frequency sub-bands, which are then employed to construct attention mechanisms across both wavelet and spatial domains. Specifically, wavelet-domain features are used to formulate query vectors, while key features are derived from the spatial domain, allowing attention weights to be computed over multi-domain representations. This design facilitates more effective fusion of spectral and spatial cues, contributing to superior reconstruction of high-resolution multispectral (HRMS) images. Complementing this, the SMCE module integrates multi-scale convolutional pathways to reinforce spatial detail extraction at varying receptive fields. Additionally, an Expert Feature Compensator is introduced to adaptively balance contributions from different scales, thereby optimizing the trade-off between local detail preservation and global contextual understanding. Comprehensive experiments conducted on standard benchmark datasets demonstrate that CDFAN achieves notable improvements over existing state-of-the-art pansharpening methods, delivering enhanced spectral–spatial fidelity and producing images with higher perceptual quality. Full article