Search Results (801)

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

It is well-established that principal leadership significantly influences student learning in developed countries, yet much less is known about how leadership practices manifest in complex systems like China’s, where rapid modernization intersects with deep-rooted educational traditions. In particular, Chinese principals face multiple challenges in balancing the implementation of educational reform policies, high parental expectations, and their own educational ideology, all within limited resources. The current study examines these challenges in Shenzhen, a city which typically manifests them through its rapid development. Specifically, we took a phenomenographic approach and interviewed the principals and staff from five prestigious primary schools to extract the key components behind the diverse school leaders’ styles and practices. Results showed that, the Chinese leadership practice model consists of five key components: mission setting, infrastructure reconstruction, teacher development, learning improvement, and educators’ networking. Although the first four components in this model align with established theories in developed countries, networking was identified as a distinctive and critical element for securing resources and fostering collaboration. These findings may broaden the scope of leadership theories and underscore the need to contextualize leadership practices based on local challenges and dynamics. It also offers practical insights for school leaders on navigating challenges to improve teacher and student outcomes. Full article

Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from Cynara cardunculus (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract (PC) with traditional bovine rennet rich in chymosin (AC) during manufacture and 60-day ripening of Caciotta cheese. Classical compositional assays (ripening index, texture profile, color, solubility) were integrated with scanning electron microscopy, three-dimensional surface reconstruction, and descriptive sensory analysis. AC cheeses displayed slower but sustained proteolysis, yielding a higher and more linear ripening index, softer body, greater solubility, and brighter, more yellow appearance. Imaging revealed a continuous protein matrix with uniformly distributed, larger pores, consistent with a dairy-like sensory profile dominated by milky and umami notes. Conversely, PC cheeses underwent rapid early proteolysis that plateaued, producing firmer, chewier curds with lower solubility and darker color. Micrographs showed a fragmented matrix with smaller, heterogeneous pores; sensory evaluation highlighted vegetal, bitter, and astringent attributes. The data demonstrate that thistle coagulant can successfully replace animal rennet but generates cheeses with distinct structural and sensory fingerprints. The optimization of process parameters is therefore required when targeting specific product styles. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Background: Surgical management of large full-thickness macular holes (MHs) remains challenging, particularly when aiming for both rapid visual recovery and consistent anatomical closure without inducing retinal trauma. This retrospective single-center study evaluated the efficacy of activated conditioned plasma (ACP) as an intraoperative coadjuvant supporting ILM (internal limiting membrane) peeling and air tamponade in the treatment of idiopathic MHs measuring 400–800 µm, under real-time intraoperative optical coherence tomography (i-OCT) guidance. Methods: Seventy eyes from fifty patients underwent pars plana vitrectomy with intraoperative ACP application. ACP, a leukocyte-poor autologous platelet concentrate, was used intraoperatively as a coadjuvant to ILM peeling and air tamponade. It facilitated the formation of a transparent fibrin membrane over the retinal surface, supporting edge approximation and promoting retinal healing. Results: The primary outcome was complete MH closure confirmed by OCT; the secondary outcome was improvement in BCVA on postoperative day 7 and during a 12-month follow-up. Anatomical closure was achieved in 98.6% of cases. On day 7, 78.6% of eyes showed a ≥ three-line BCVA improvement, with mean BCVA increasing from 0.25 ± 0.21 to 0.69 ± 0.20 (p < 0.001). These outcomes remained stable throughout the follow-up. No significant intraoperative or postoperative complications were observed. Conclusions: The combination of ACP and i-OCT appears to be a safe and effective strategy for anatomical foveolar reconstruction, enabling early visual recovery while minimizing inflammation and fibrotic scarring associated with conventional techniques. Full article

Background and Clinical Significance: Juvenile ossifying fibroma (JOF) is a rare, benign, but locally aggressive fibro-osseous neoplasm that primarily affects the craniofacial skeleton of children and adolescents. Early surgical intervention is often required due to the lesion’s rapid growth and potential for significant facial deformity. Long-term functional and esthetic rehabilitation following maxillary resection in early childhood remains a clinical challenge. Case Presentation: This case reports a unique long-term follow-up of a 22-year-old female patient who underwent partial maxillary resection at the age of five due to JOF. Initial reconstructive efforts failed, necessitating a removable prosthesis to restore function and appearance. The patient experienced persistent self-consciousness and social withdrawal during adolescence, attributed to altered facial esthetics and repeated surgical disappointment. Nevertheless, prosthetic rehabilitation significantly improved mastication, phonetics, facial symmetry, and psychological well-being. Conclusions: The enduring psychosocial and functional impact of early maxillary resection for JOF and the pivotal role of prosthodontic management in long term rehabilitation are highlighted. A multidisciplinary approach that includes psychological support is suggested. This case report is among the few reports documenting long-term prosthetic outcomes for pediatric JOF patients extending into adulthood. Full article

Against the backdrop of the rapid advancement of intelligent speech interaction and holographic display technologies, this paper introduces an interactive holographic display system. This paper applies 2D-to-3D technology to acquisition work and uses a Complex-valued Convolutional Neural Network Point Cloud Gridding (CCNN-PCG) algorithm to generate a computer-generated hologram (CGH) with depth information for application in point cloud data. During digital human hologram building, 2D-to-3D conversion yields high-precision point cloud data. The system uses ChatGLM for natural language processing and emotion-adaptive responses, enabling multi-turn voice dialogs and text-driven model generation. The CCNN-PCG algorithm reduces computational complexity and improves display quality. Simulations and experiments show that CCNN-PCG enhances reconstruction quality and speeds up computation by over 2.2 times. This research provides a theoretical framework and practical technology for holographic interactive systems, applicable in virtual assistants, educational displays, and other fields. Full article

As distant hybridization has profound implications for evolutionary biology, aquaculture, and biodiversity conservation, this study aims to elucidate patterns of maternal inheritance, genetic divergence, and phylogenetic relationships by synthesizing mitochondrial genome (mitogenome) data from 74 distant hybrid fish species. These hybrids span diverse taxa, including 48 freshwater and 26 marine species, with a focus on Cyprinidae (n = 35) and Epinephelus (n = 14), representing the most frequently hybridized groups in freshwater and marine systems, respectively. Mitogenome lengths were highly conserved (15,973 to 17,114 bp); however, the genetic distances between hybrids and maternal species varied from 0.001 to 0.17, with 19 hybrids (25.7%) showing distances >0.02. Variable sites in these hybrids were randomly distributed but enriched in hypervariable regions, such as the D-loop and NADH dehydrogenase subunits 1, 3 and 6 (ND2, ND3, and ND6) genes, likely reflecting maternal inheritance (reported in Cyprinus carpio × Carassius auratus). Moreover, these genes were under purifying selection pressure, revealing their conserved nature. Phylogenetic reconstruction using complete mitogenomes revealed three distinct clades in hybrids: (1) Acipenseriformes, (2) a freshwater cluster dominated by Cypriniformes and Siluriformes, and (3) a marine cluster comprising Centrarchiformes, Pleuronectiformes, Scombriformes, Cichliformes, Anabantiformes, Tetraodontiformes, Perciformes, and Salmoniformes. The prevalence of Cyprinidae hybrids underscores their importance in aquaculture for hybridization, where traits such as rapid growth and disease resistance are enhanced. In contrast, marine hybrids are valued for their market value and adaptability. While mitogenome data robustly support maternal inheritance in most cases, exceptions suggest complex mechanisms, such as doubly uniparental inheritance (DUI), in distantly related crosses. Moreover, AT-skew of genes in hybrids revealed a paternal leakage of traits in mitogenomes. This study also highlights ecological risks, such as genetic swamping in native populations, emphasizing the need for responsible hybridization practices. These findings advance our understanding of the role of hybridization in fish evolution and aquaculture, providing a genomic framework and policy recommendations for optimizing breeding programs, hybrid introduction, and mitigating conservation challenges. Full article

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

To address the limitations of traditional tunnel structural plane modeling—such as low automation, insufficient smoothness, and poor adaptability to real construction environments—this study proposes a novel three-dimensional (3D) modeling framework based on B-spline interpolation combined with deep learning. The method first employs YOLOv5 for rapid detection of structural regions and DeepLabV3+ for precise boundary segmentation, followed by skeleton extraction and coordinate transformation to obtain spatial structural traces. Finally, B-spline interpolation is applied across multiple tunnel sections to construct continuous 3D surfaces. In model training and testing, the segmentation network achieved an F1 score of 94.01%, and the final modeling accuracy demonstrated a mean relative error (MRE) below 2.5%, confirming the reliability of the geometric reconstruction. Additionally, the proposed method was applied to excavation face images from the Paiyashan Tunnel, where multiple structural surfaces were successfully reconstructed in 3D, validating the approach’s applicability and robustness in real geological conditions. Compared to traditional triangulated or linear surface methods, the proposed approach achieves higher smoothness, better geological continuity, and improved automation, making it suitable for real-world geotechnical applications. Full article

This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and traditional Structure-from-Motion (SfM) photogrammetry. The research targets artifacts smaller than 5 cm, characterized by complex geometries and reflective surfaces that pose challenges for conventional recording methods. To address the limitations of traditional methods without resorting to the high costs associated with laser scanning, this study explores NeRF and GS as cost-effective and efficient alternatives. A comprehensive experimental framework was established, incorporating ground-truth data obtained using a metrological articulated arm and a rigorous quantitative evaluation based on root mean square (RMS) error, Chamfer distance, and point cloud density. The results indicate that while NeRF outperforms GS in terms of geometric fidelity, both techniques still exhibit lower accuracy compared to SfM, particularly in preserving fine geometric details. Nonetheless, NeRF demonstrates strong potential for rapid, high-quality 3D documentation suitable for visualization and dissemination purposes in cultural heritage. These findings highlight both the current capabilities and limitations of neural rendering techniques for archaeological documentation and suggest promising future research directions combining AI-based models with traditional photogrammetric pipelines. Full article

The transport of intensity equation enables quantitative phase imaging from only two axially displaced intensity images, facilitating the characterization of low-contrast samples like cells and microorganisms. However, the rapid selection of the correct defocused planes, crucial for real-time phase imaging of dynamic events, remains challenging. Additionally, the different images are normally acquired sequentially, further limiting phase-reconstruction speed. Here, we report on a system that addresses these issues and enables user-tuned defocusing with snapshot phase retrieval. Our approach is based on combining multi-color pulsed illumination with acousto-optic defocusing for microsecond-scale chromatic aberration control. By illuminating each plane with a different color and using a color camera, the information to reconstruct a phase map can be gathered in a single acquisition. We detail the fundamentals of our method, characterize its performance, and demonstrate live phase imaging of a freely moving microorganism at speeds of 150 phase reconstructions per second, limited only by the camera’s frame rate. Full article

With the rapid development of science and technology, attackers have invented more and more ways to hide malicious information. Hidden malicious information often contains a large number of malicious codes and malicious scripts, which can be hidden in legitimate software and reconstructed to be executed as the software is executed. In recent years, phishing software has become popular at home and abroad, causing fraud to occur frequently. Among various carriers with high redundancy, images are often used by attackers to hide malicious information because they are often used as information transmission carriers and highly redundant storage. This paper aims to explore how attackers hide malicious information in images and use a convolutional neural network (CNN) framework with acceleration based on the analysis of the Edge of Stability (EOS) phenomenon to detect mobile phishing software. To design a machine learning approach to solve the problem, we summarize the characteristics of nine presented mainstream malicious information hiding methods and present a CNN framework that maintains a high initial learning rate while preventing the gradient from exploding in EOS. R-tree is used to speed up the search for nearby pixels that contain malicious information. The CNN model generated by training under this framework can reach an accuracy of $98.53\%$ and has been well implemented in mobile terminals. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing offers wide coverage, existing optical and Synthetic Aperture Radar (SAR)-based techniques face challenges in direct volume estimation due to resolution constraints and rapid terrain changes. This study proposes a Super-Resolution Shape from Shading (SRSFS) approach enhanced by a Non-local Piecewise-smooth albedo Constraint (NPC), hereafter referred to as NPC SRSFS, to estimate debris-flow erosion volume using single high-resolution optical satellite imagery. By integrating publicly available global Digital Elevation Model (DEM) data as prior terrain reference, the method enables accurate post-disaster topography reconstruction from a single optical image, thereby reducing reliance on stereo imagery. The NPC constraint improves the robustness of albedo estimation under heterogeneous surface conditions, enhancing depth recovery accuracy. The methodology is evaluated using Gaofen-6 satellite imagery, with quantitative comparisons to aerial Light Detection and Ranging (LiDAR) data. Results show that the proposed method achieves reliable terrain reconstruction and erosion volume estimates, with accuracy comparable to airborne LiDAR. This study demonstrates the potential of NPC SRSFS as a rapid, cost-effective alternative for post-disaster debris-flow assessment. Full article