Search Results (6,980)

Background/Objectives: The recent development of four-dimensional X-ray velocimetry (4DXV) technology (three-dimensional space and time) provides a unique opportunity to obtain preclinical quantitative functional lung images. Only single-scan measurements in non-survival studies have been obtained to date; thus, methodologies enabling animal survival for repeated imaging to be accomplished over weeks or months from the same animal would establish new opportunities for the assessment of pathophysiology drivers and treatment response in advanced preclinical drug-screening efforts. Methods: An anesthesia protocol developed for animal recovery to allow for repetitive, longitudinal scanning of individual animals over time. Test–retest imaging scans from the lungs of healthy mice were performed over 8 weeks to assess the repeatability of scanner-derived quantitative imaging metrics and variability. Results: Using a murine model of fibroproliferative lung disease, this longitudinal scanning approach captured heterogeneous progressive changes in pulmonary function, enabling the visualization and quantitative measurement of averaged whole lung metrics and spatial/regional change. Radiation dosimetry studies evaluated the effects of imaging acquisition protocols on X-ray dosage to further adapt protocols for the minimization of radiation exposure during repeat imaging sessions using these newly developed image acquisition protocols. Conclusions: Overall, we have demonstrated that the 4DXV advanced imaging scanner allows for repeat measurements from the same animal over time to enable the high-resolution, noninvasive mapping of quantitative lung airflow dysfunction in mouse models with heterogeneous pulmonary disease. The animal anesthesia and image acquisition protocols described will serve as the foundation on which further applications of the 4DXV technology can be used to study a diverse array of murine pulmonary disease models. Together, 4DXV provides a novel and significant advancement for the longitudinal, noninvasive interrogation of pulmonary disease to assess spatial/regional disease initiation, progression, and response to therapeutic interventions. Full article

In recent years, the demand for both high accuracy and real-time performance in 3D object detection has increased alongside the advancement of autonomous driving technology. While multimodal methods that integrate LiDAR and camera data have demonstrated high accuracy, these methods often have high computational costs and latency. To address these issues, we propose an efficient 3D object detection network that integrates three key components: a DepthWise Lightweight Encoder (DWLE) module for efficient feature extraction, an Efficient LiDAR Image Fusion (ELIF) module that combines channel attention with cross-modal feature interaction, and a Mixture of CNN and Point Transformer (MCPT) module for capturing rich spatial contextual information. Experimental results on the KITTI dataset demonstrate that our proposed method outperforms existing approaches by achieving approximately 0.6% higher 3D mAP, 7.6% faster inference speed, and 17.0% fewer parameters. These results highlight the effectiveness of our approach in balancing accuracy, speed, and model size, making it a promising solution for real-time applications in autonomous driving. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods. Full article

Cropland abandonment, a global challenge, necessitates comprehensive monitoring to achieve the zero hunger goal. Prior monitoring approaches to cropland abandonment often face constraints in resolution, time series, drivers, prediction, or a combination of these. Here, we proposed an artificial intelligence framework to comprehensively monitor cropland abandonment and tested the framework in Hengyang City, China. Specifically, we first mapped land cover at 30 m resolution from 1985 to 2023 using Landsat, stable sample points, and a machine learning model. Subsequently, we constructed the extent, time, and frequency of cropland abandonment from 1986 to 2022 by analyzing pixel-level land-use trajectories. Finally, we quantified the drivers of cropland abandonment using machine learning models and predicted the spatial distribution of cropland abandonment risk from 2032 to 2062. Our results indicated that the abandonment maps achieved overall accuracies of 0.88 and 0.78 for identifying abandonment locations and timing, respectively. From 1986 to 2022, the proportion of cropland abandonment ranged between 0.15% and 4.06%, with an annual average abandonment rate of 1.32%. Additionally, the duration of abandonment varied from 2 to 38 years, averaging approximately 14 years, indicating widespread cropland abandonment in the study area. Furthermore, 62.99% of the abandoned cropland experienced abandonment once, 27.17% experienced it twice, and only 0.23% experienced it five times or more. Over 50% of cropland abandonment remained unreclaimed or reused. During the study period, tree cover, soil pH, soil total phosphorus, potential crop yield, and the multiresolution index of valley bottom flatness emerged as the five most important environmental covariates, with relative importances of 0.087, 0.074, 0.068, 0.050, and 0.043, respectively. Temporally, cropland abandonment in 1992 was influenced by transportation inaccessibility and low agricultural productivity, soil quality degradation became an additional factor by 2010, and synergistic effects of all three drivers were observed from 2012 to 2022. Notably, most cropland had a low abandonment risk (mean: 0.36), with only 0.37% exceeding 0.7, primarily distributed in transitional zones between cropland and non-cropland. Future risk predictions suggested a gradual decline in both risk values and the spatial extent of cropland abandonment from 2032 to 2062. In summary, we developed a comprehensive framework for monitoring cropland abandonment using artificial intelligence technology, which can be used in national or regional land-use policies, warning systems, and food security planning. Full article

The recent growth of generative artificial intelligence (AI) has brought new possibilities and revolutionary applications in many fields. It has also, however, created important ethical and security issues, especially with the abusive use of deepfakes, which are artificial media that can propagate very realistic but false information. This paper provides an extensive bibliometric, statistical, and trend analysis of deepfake research in the age of generative AI. Utilizing the Web of Science (WoS) database for the years 2014–2024, the research identifies key authors, influential publications, collaboration networks, and leading institutions. Biblioshiny (Bibliometrix R package, University of Naples Federico II, Naples, Italy) and VOSviewer (version 1.6.20, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) are utilized in the research for mapping the science production, theme development, and geographical distribution. The cutoff point of ten keyword frequencies by occurrence was applied to the data for relevance. This study aims to provide a comprehensive snapshot of the research status, identify gaps in the knowledge, and direct upcoming studies in the creation, detection, and mitigation of deepfakes. The study is intended to help researchers, developers, and policymakers understand the trajectory and impact of deepfake technology, supporting innovation and governance strategies. The findings highlight a strong average annual growth rate of 61.94% in publications between 2014 and 2024, with China, the United States, and India as leading contributors, IEEE Access among the most influential sources, and three dominant clusters emerging around disinformation, generative models, and detection methods. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

To break through the bottleneck in the mapping of the mechanoluminescent (ML) intensity field to the strain field, a quantification method for full-field strain measurement based on pixel-level data fusion is proposed, integrating ML imaging with digital image correlation (DIC) to achieve precise reconstruction of the strain field. Experiments are conducted using aluminum alloy specimens coated with ML film sensor on their surfaces. During the tensile process, ML images of the films and speckle images of the specimen backsides are simultaneously acquired. Combined with DIC technology, high-precision full-field strain distributions are obtained. Through spatial registration and region matching algorithms, a quantitative calibration model between ML intensity and DIC strain is established. The research results indicate that the ML intensity and DIC strain exhibit a significant linear correlation (R² = 0.92). To verify the universality of the model, aluminum alloy notched specimen tests show that the reconstructed strain field is in good agreement with the DIC and finite element analysis results, with an average relative error of 0.23%. This method enables full-field, non-contact conversion of ML signals into strain distributions with high spatial resolution, providing a quantitative basis for studying ML response mechanisms under complex loading. Full article

This study aimed to evaluate how different insertion angles of titanium orthodontic miniscrews (30°, 45°, and 90°) influence stress distribution and displacement in surrounding alveolar bone using three-dimensional finite element analysis (FEA), with a focus on biomechanical outcomes at the titanium–bone interface. The 90° insertion angle generated the highest stress in cortical bone (58.2 MPa) but the lowest displacement (0.023 mm), while the 30° angle produced lower stress (36.4 MPa) but greater displacement (0.052 mm). The 45° angle represented a compromise, combining moderate stress (42.7 MPa) and displacement (0.035 mm). This simulation-based study was conducted between January and April 2025 at the Department of Orthodontics, Kocaeli Health and Technology University. A standardized 3D mandibular bone model (2 mm cortical and 13 mm cancellous layers) was constructed, and Ti-6Al-4V miniscrews (1.6 mm × 8 mm) were virtually inserted at 30°, 45°, and 90°. A horizontal orthodontic load of 2 N was applied, and von Mises stress and displacement values were calculated in ANSYS Workbench. Stress patterns were visualized using color-coded maps. The 90° insertion angle generated the highest von Mises stress in cortical bone (50.6 MPa), with a total maximum stress of 58.2 MPa, followed by 45° (42.7 MPa) and 30° (36.4 MPa) insertions (p < 0.001). Stress was predominantly concentrated at the cortical entry point, especially in the 90° model. In terms of displacement, the 90° group exhibited the lowest mean displacement (0.023 ± 0.002 mm), followed by 45° (0.035 ± 0.003 mm) and 30° (0.052 ± 0.004 mm), with statistically significant differences among all groups (p < 0.001). The 45° angle showed a balanced biomechanical profile, combining moderate stress and displacement values, as confirmed by post hoc analysis. From a biomimetics perspective, understanding how insertion angle affects bone response provides insights for designing bio-inspired anchorage systems. By simulating natural stress dissipation, this study demonstrates that insertion angle strongly modulates miniscrew performance. Vertical placement (90°) ensures rigidity but concentrates cortical stress, whereas oblique placement, particularly at 45°, offers a balanced compromise with adequate stability and reduced stress. These results emphasize that beyond material properties, surgical parameters such as insertion angle are critical for clinical success. Full article

Smart tourism destinations, shaped by the integration of tourism and information technology, have become a central theme in international academic research. This study employs bibliometric methods using CiteSpace to conduct co-authorship, co-citation, keyword co-occurrence, and burst analyses, with the aim of mapping the knowledge structure and research evolution of the field. Drawing on 232 articles from the Web of Science Core Collection (2013–2025), the results reveal a shift from technology-centered approaches toward themes of visitor experience, collaborative governance, and sustainable development. The Universitat d’Alacant (Spain) and The Hong Kong Polytechnic University (China) have emerged as leading research hubs, with Ivars-Baidal and colleagues as major contributors. Foundational studies by Buhalis and Gretzel continue to shape the domain. Keyword trends highlight increasing attention to technological efficiency and sustainable ethics. Overall, the study traces the developmental trajectory of smart tourism destinations, proposes a systematic knowledge framework, and identifies future directions for theoretical integration and methodological innovation. The findings provide both conceptual insights for academic research and strategic guidance for destination governance and policy. Full article

The size and mass of fish are crucial parameters in aquaculture management. However, existing research primarily focuses on conducting fish size and mass estimation under ideal conditions, which limits its application in actual aquaculture scenarios with complex water quality and fluctuating lighting. A non-contact size and mass measurement framework is proposed for complex underwater environments, which integrates the improved FishKP-YOLOv11 module based on YOLOv11, stereo vision technology, and a Random Forest model. This framework fuses the detected 2D key points with binocular stereo technology to reconstruct the 3D key point coordinates. Fish size is computed based on these 3D key points, and a Random Forest model establishes a mapping relationship between size and mass. For validating the performance of the framework, a self-constructed grass carp dataset for key point detection is established. The experimental results indicate that the mean average precision (mAP) of FishKP-YOLOv11 surpasses that of diverse versions of YOLOv5–YOLOv12. The mean absolute errors (MAEs) for length and width estimations are 0.35 cm and 0.10 cm, respectively. The MAE for mass estimations is 2.7 g. Therefore, the proposed framework is well suited for application in actual breeding environments. Full article

Remote sensing technologies are increasingly integrated with autonomous robotic platforms to enhance data-driven decision-making in precision agriculture. Rather than replacing conventional platforms such as satellites or UAVs, autonomous ground robots complement them by enabling high-resolution, site-specific observations in real time, especially at the plant level. This review analyzes how remote sensing sensors—including multispectral, hyperspectral, LiDAR, and thermal—are deployed via robotic systems for specific agricultural tasks such as canopy mapping, weed identification, soil moisture monitoring, and precision spraying. Key benefits include higher spatial and temporal resolution, improved monitoring of under-canopy conditions, and enhanced task automation. However, the practical deployment of such systems is constrained by terrain complexity, power demands, and sensor calibration. The integration of artificial intelligence and IoT connectivity emerges as a critical enabler for responsive, scalable solutions. By focusing on how autonomous robots function as mobile sensor platforms, this article contributes to the understanding of their role within modern precision agriculture workflows. The findings support future development pathways aimed at increasing operational efficiency and sustainability across diverse crop systems. Full article

As Virtual Try-On (VTON) technology matures, 2D VTON methods based on diffusion models can now rapidly generate diverse and high-quality try-on results. However, with rising user demands for realism and immersion, many applications are shifting towards 3D VTON, which offers superior geometric and spatial consistency. Existing 3D VTON approaches commonly face challenges such as barriers to practical deployment, substantial memory requirements, and cross-view inconsistencies. To address these issues, we propose an efficient 3D VTON framework with robust multi-view consistency, whose core design is to decouple the monolithic 3D editing task into a four-stage cascade as follows: (1) We first reconstruct an initial 3D scene using 3D Gaussian Splatting, integrating the SMPL-X model at this stage as a strong geometric prior. By computing a normal-map loss and a geometric consistency loss, we ensure the structural stability of the initial human model across different views. (2) We employ the lightweight CatVTON to generate 2D try-on images, that provide visual guidance for the subsequent personalized fine-tuning tasks. (3) To accurately represent garment details from all angles, we partition the 2D dataset into three subsets—front, side, and back—and train a dedicated LoRA module for each subset on a pre-trained diffusion model. This strategy effectively mitigates the issue of blurred details that can occur when a single model attempts to learn global features. (4) An iterative optimization process then uses the generated 2D VTON images and specialized LoRA modules to edit the 3DGS scene, achieving 360-degree free-viewpoint VTON results. All our experiments were conducted on a single consumer-grade GPU with 24 GB of memory, a significant reduction from the 32 GB or more typically required by previous studies under similar data and parameter settings. Our method balances quality and memory requirement, significantly lowering the adoption barrier for 3D VTON technology. Full article

MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patients. However, in practical application, it has disadvantages such as large computation and difficulty in parallel. Under the architecture of multi-core shared memory, using block pretreatment, color block scheduling NUFFT convolution interpolation offers a parallel solution, and then using a static linked list solves the problem of large memory requirements after the parallel solution on the basis of multithreading to cycle through more source code versions. Then, manual vectorization, such as processing, using short vector components, further accelerates the process. Through a series of optimizations, the final Random, Radial, and Spiral dataset obtained an acceleration effect of 273.8×, 291.8× and 251.7×, respectively. Full article