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Offshore wind turbines are usually founded on monopiles. During the operation period, the structure is subjected to complex lateral loading from wind, wave and current. The soils surrounding those monopiles may deform with increasing the number of loading cycles, leading to tilting of the whole structure; hence, it is vital to carry out physical model tests to examine the long-term performance of monopiles. This study proposes an innovative experimental setup for centrifuge modelling of the response of monopiles under complex lateral loading. Hydraulic actuators are adopted to apply lateral loads on model pile, and electrohydraulic servo-valves and associated controllers are used to achieve a closed loop position or load control. A carefully designed spherical hinge and load bars are used to connect the model pile and actuator shafts. This enables that the pile can rotate freely and can move vertically freely. A centrifuge test on a winged monopole subjected to perpendicular lateral loading was carried out at 100g. The experimental results shed light on pile responses in the cyclic loading and constant loading directions.

To ensure maritime search and rescue safety, this study combined a geographic information system and a fuzzy analytic hierarchy process to map and evaluate the difficulties of maritime search and rescue in the South China Sea. To construct a maritime search and rescue difficulty index system, nine factors related to the natural environment and human power that may influence maritime search and rescue were selected, and difficulty indices were calculated according to the corresponding calculation method. The fuzzy analytic hierarchy process was then used to determine the weight of each index, and the linear weighting method was used to obtain the maritime search and rescue difficulty value of the South China Sea. The difficulty values were divided into 10 levels and the spatial distribution characteristics were analyzed. The results show that: 1) under the influence of the natural environment, the difficulty of search and rescue in the sea area of the South China Sea had the characteristics of a V-shaped distribution, and the difficulty level gradually decreased from northeast to southwest. 2) Under the influence of humanistic search and rescue forces, the overall difficulty of search and rescue in the study area was the "NE-SW belt" type. The area with high difficulty levels was mainly located on the line connecting the northeast to the southwest of the study area, and the difficulty decreased in an irregular circle on both sides of the connecting line. 3) Considering these two factors, the difficulty of search and rescue was relatively high around some islands in the South China Sea, because of the influence of severe weather events, such as typhoons, and their large distance from the search and rescue base. The difficulty values in the far seas have "NE-SW decreasing" spatial distribution pattern, with the highest values in the areas extending from the northeast to the southwest of the study area, then decreasing to the northwest and southeast. According to statistics, approximately 21.1% of the sea areas in the South China Sea have search and rescue difficulties greater than level 6, and the construction of search and rescue forces should be optimized accordingly.

This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffusion process, this approach significantly improves the effectiveness and efficiency of enhancing low-quality fundus images. The model achieves high-precision enhancement of retinal vessel details while substantially reducing the number of parameters. Comprehensive tests on publicly available datasets show that the suggested approach surpasses various advanced low-light image enhancement methods in both quantitative and qualitative assessments. [ABSTRACT FROM AUTHOR]

To solve the problems of chaotic information management and difficult traceability in the manufacturing process of transformer coils, a traceability and management method oriented towards the manufacturing process of transformer coils has been proposed. This method integrates industrial internet identification resolution and extension of Petri net modeling theory. A comprehensive identification and resolution framework for coil manufacturing processes has been constructed. In this manuscript, the authors proposed an industrial data-sharing space based on the producer-consumer model with unified coding identification. This enables information sharing for all resources, including personnel, machinery, materials, methods, environment, and measurements. A method for modeling extensible identification primitives of coil manufacturing process information was proposed, which formalizes the correlation and data structure of process information. A Petri net model for the comprehensive acquisition and integration of elemental information in coil manufacturing processes, as well as a mathematical model for quality traceability, were constructed, thereby forming a complete path for quality traceability information. Finally, based on the method proposed above, a software and hardware environment for identification and traceability for coil manufacturing was established. Taking a certain type of coil as an example, validation was carried out; the results indicate a significant enhancement in the production management and information traceability capabilities of the coil production workshop. This study provides reference and guidance for the process traceability management of power equipment manufacturing. [ABSTRACT FROM AUTHOR]

Interactive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy with limited image data, demonstrating significant potential in clinical applications. Typically, for each new click provided by the user, conventional interactive segmentation methods reprocess the entire network by re-inputting the click into the segmentation model, which greatly increases the user's interaction burden and deviates from the intended goal of interactive segmentation tasks. To address this issue, we propose an efficient segmentation network, ESM-Net, for interactive medical image segmentation. It obtains high-quality segmentation masks based on the user's initial clicks, reducing the complexity of subsequent refinement steps. Recent studies have demonstrated the strong performance of the Mamba model in various vision tasks; however, its application in interactive segmentation remains unexplored. In our study, we incorporate the Mamba module into our framework for the first time and enhance its spatial representation capabilities by developing a Spatial Augmented Convolution (SAC) module. These components are combined as the fundamental building blocks of our network. Furthermore, we designed a novel and efficient segmentation head to fuse multi-scale features extracted from the encoder, optimizing the generation of the predicted segmentation masks. Through comprehensive experiments, our method achieved state-of-the-art performance on three medical image datasets. Specifically, we achieved 1.43 NoC@90 on the Kvasir-SEG dataset, 1.57 NoC@90 on the CVC-ClinicDB polyp segmentation dataset, and 1.03 NoC@90 on the ADAM retinal disk segmentation dataset. The assessments on these three medical image datasets highlight the effectiveness of our approach in interactive medical image segmentation. [ABSTRACT FROM AUTHOR]

SrAl2O4:Eu2+, Dy3+ (SAED) is one of the most popular materials for information storage and night display applications because it has a wide excitation range and long afterglow duration. However improving the hydrolytic resistance of SAED remains a challenge. In this study, the SAED is presented to be the ceramic type by a solid‐state reaction in a vacuum ambiance. The achieved SAED ceramic has 8200 mcd m−2 initial luminescence intensity. It can also obtain long‐persistent luminescence after UV light irradiation even soaking in water for more than 30 days. This ceramic demonstrates sustained imaging capability irradiated under 365 nm UV light and X‐ray, as well as erasability and reproducibility of storage by lighting and heating. These results indicate the promising applications of SAED ceramics in optical information storage and night display in complex environments. The phase evolution in the SAED ceramic is identified directly in the micromorphology for the first time based on scanning electron microscopy coupled with a cathodoluminescence system. [ABSTRACT FROM AUTHOR]

In order to effectively curb the rapid growth trend in Spartina alterniflora in coastal cities of China, this study proposes an innovative mechanical equipment design scheme for eradicating Spartina alterniflora. Based on literature analysis and field research, the AHP (analytic hierarchy process) model is constructed to quantify and prioritize the diverse needs of users for control equipment. Subsequently, the House of Quality (HOQ) in QFD (Quality Function Deployment) is used to analyze the key components and structure of the equipment to ensure its performance and feasibility in practical applications. Finally, combined with the Theory of Inventive Problem Solving (TRIZ), the potential problems encountered in the structural design of the equipment are analyzed, and the corresponding creative principles are applied to solve the contradictions and complete the optimal scheme design. This study, via the acquisition of user needs and further analysis of the machinery's structure, proposes a scheme that can address many problems related to Spartina alterniflora in China and provide new technical ideas for the field of wetland environmental protection. [ABSTRACT FROM AUTHOR]

All‐solid‐state battery (ASSB) is a potential future energy storage technology to power the future world. However, the solid–solid contacts among the various components in ASSB often require a high stack pressure which is unrealistic in practical applications. Here it is shown that by carbon‐coating Sb2Se3 (Sb2Se3@C) nanorods, a record low stack pressure is attained, and the Sb2Se3@C electrode exhibits outstanding performance of 862 mAh g−1 over 50 cycles at 0.1 A g−1 when it is operated at 5 MPa stack pressure in an ASSB using LiIn anode and Li10Si0.3PS6.7Cl1.8 solid‐state electrolytes. Furthermore, a record high active material loading (the mass fraction of Sb2Se3@C being 0.7 in composite cathode) is achieved, and the Sb2Se3@C based ASSB delivers a reversible capacity of 2.6 mAh cm−2 at 0.1 A g−1 after 100 cycles. This work provides a strategy to significantly increase the cathode active material loading and dramatically reduce the stack pressure of ASSBs, which brings it one step closer toward the practical implementation of ASSBs for energy storage applications. [ABSTRACT FROM AUTHOR]

China holds the top position globally in apple production and consumption. Detecting diseases during the planting process is crucial for increasing yields and promoting the rapid development of the apple industry. This study proposes a lightweight algorithm for apple leaf disease detection in natural environments, which is conducive to application on mobile and embedded devices. Our approach modifies the YOLOv8n framework to improve accuracy and efficiency. Key improvements include replacing conventional Conv layers with GhostConv and parts of the C2f structure with C3Ghost, reducing the model's parameter count, and enhancing performance. Additionally, we integrate a Global attention mechanism (GAM) to improve lesion detection by more accurately identifying affected areas. An improved Bi-Directional Feature Pyramid Network (BiFPN) is also incorporated for better feature fusion, enabling more effective detection of small lesions in complex environments. Experimental results show a 32.9% reduction in computational complexity and a 39.7% reduction in model size to 3.8 M, with performance metrics improving by 3.4% to a mAP@0.5 of 86.9%. Comparisons with popular models like YOLOv7-Tiny, YOLOv6, YOLOv5s, and YOLOv3-Tiny demonstrate that our YOLOv8n–GGi model offers superior detection accuracy, the smallest size, and the best overall performance for identifying critical apple diseases. It can serve as a guide for implementing real-time crop disease detection on mobile and embedded devices. [ABSTRACT FROM AUTHOR]

Using traditional algorithms to identify persistent scatterer (PS) points is challenging during bridge construction because of short-term changes at construction sites, such as earthworks, as well as the erection and dismantling of temporary structures. To address this issue, this study proposes a PS point selection method based on statistical theory and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithm. This method adopts amplitude information as an incoherence evaluation indicator. Furthermore, the statistical median of the amplitude dispersion index and amplitude mean is screened twice to extract a set of candidate points, including PS points that exhibit stable backscattering over long durations. Temporal coherence is simultaneously used as the coherence evaluation indicator. Another candidate point set is obtained by extracting high-coherence PS points using the GMM-EM algorithm. These sets of candidate points are then combined to obtain a final PS points set. In the experiment, the deformation monitoring of the under-construction Shenzhen-Zhongshan Cross-Sea Bridge in China was selected as a case study, with 28 Sentinel-1A images used as the data source for PS selection and deformation information extraction. The results show that the proposed method enhanced the density and quality of PS points on the under-construction cross-sea bridge compared to existing PS selection methods, thus offering higher reliability. Deformation analysis further revealed fluctuating deformation trends at characteristic points of the Shenzhen-Zhongshan Cross-Sea Bridge, indicating the occurrence of elastic deformation during its construction. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]