Search Results (133)

This paper presents the design, fabrication, and experimental evaluation of a capacitive micromachined ultrasonic transducer (CMUT) linear array for non-contact thickness measurement of marine engineering structures. A 16-element CMUT array was fabricated using a silicon–silicon wafer bonding process, and encapsulated in polyurethane to ensure acoustic impedance matching and environmental protection in underwater conditions. The acoustic performance of the encapsulated CMUT was characterized using standard piezoelectric transducers as reference. The array achieved a transmitting sensitivity of 146.82 dB and a receiving sensitivity of −229.55 dB at 1 MHz. A complete thickness detection system was developed by integrating the CMUT array with a custom transceiver circuit and implementing a time-of-flight (ToF) measurement algorithm. To evaluate environmental robustness, systematic experiments were conducted under varying water temperatures and salinity levels. The results demonstrate that the absolute thickness measurement error remains within ±0.1 mm under all tested conditions, satisfying the accuracy requirements for marine structural health monitoring. The results validate the feasibility of CMUT-based systems for precise and stable thickness measurement in underwater environments, and support their application in non-destructive evaluation of marine infrastructure. Full article

To enhance the generalization of networks and avoid redundant training efforts, cross-image hyperspectral anomaly detection (HAD) based on deep learning has been gradually studied in recent years. Cross-image HAD aims to perform anomaly detection on unknown hyperspectral images after a single training process on the network, thereby improving detection efficiency in practical applications. However, the existing approaches may require additional supervised information or stacking of networks to improve model performance, which may impose high demands on data or hardware in practical applications. In this paper, a simple and lightweight unsupervised cross-image HAD method called Variational Joint Discrimination Network (VJDNet) is proposed. We leverage the reconstruction and distribution representation ability of the variational autoencoder (VAE), learning the global and local discriminability of anomalies jointly. To integrate these representations from the VAE, a probability distribution joint discrimination (PDJD) module is proposed. Through the PDJD module, the VJDNet can directly output the anomaly score mask of pixels. To further facilitate the unsupervised paradigm, a sample pair generation module is proposed, which is able to generate anomaly samples and background representation samples tailored for the cross-image HAD task. The experimental results show that the proposed method is able to maintain the detection accuracy with only a small number of parameters. Full article

Low-carbon restructuring serves as a critical strategy for enterprises to achieve the “dual-carbon” target and foster sustainable development, whereas supply chain resilience is essential for maintaining competitiveness in complex environments. Based on the data of Chinese A-share listed companies in the manufacturing industry from 2011 to 2023, this paper empirically examines the relationship between low-carbon restructuring, R&D investment, and supply chain resilience. This study reveals a U-shaped relationship between low-carbon restructuring and supply chain resilience, with an inflection point at approximately 2.34. R&D investment significantly strengthens supply chain resilience and positively moderates the relationship by accelerating technological synergies and optimizing resource allocation. Further analysis shows that heavily polluted industries face more pressure in the early stage of low-carbon restructuring compared to non-heavily polluted industries, but R&D investment has a more significant moderating effect on heavily polluted industries. The prediction results based on the Holt–Winters model show that the level of low-carbon restructuring in China’s manufacturing industry will increase steadily in the next seven years, with an average annual growth rate of about 0.021. These new findings are important for managers and researchers to improve supply chain resilience during the low-carbon transition process. Full article

This study focuses on step deformation landslides, conducting spatiotemporal analysis of landslide deformation and instability trends. First, the target landslide area is selected, and geological and precipitation data, along with historical displacement data from monitoring points, are collected. The slope single-change-point analysis method is then employed, combined with landslide profile data, to extract key features from the monitoring data. Next, Small BAseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology is applied to obtain satellite images of the study area. These images, together with the extracted data features, are used to draw the spatiotemporal baseline of the target landslide, completing the spatiotemporal analysis. Finally, a landslide prediction model is developed, and its prediction error is corrected using an Extreme Learning Machine (ELM) neural network. The refined prediction results serve as the basis for analyzing the landslide deformation coefficient, enabling the determination of the landslide instability trend. The experimental results show that step deformation landslides exhibit significant spatiotemporal variability and a short stability period throughout the year. The analytical methods designed in this study outperform traditional methods, providing more reliable results for predicting landslide instability trends. Full article

A novel polyhedron-based anionic Er-MOF with three types of cages and abundant open metal sites (OMSs) and Lewis base sites (LBSs) has been successfully synthesized. The inorganic secondary unit possesses a rarely reported six-connected three-nucleated rare-earth cluster, and the overall framework shows a new (3,3,6)-connected topology. The Er-MOF has good fluorescence selectivity and anti-interference performance with Fe³⁺ and Cu²⁺. In addition, benefiting from the anionic framework, nanoscale cavity and small window size of the Er-MOF, the composite RhB@Er-MOF has been synthesized by in situ encapsulation of the cationic dye Rhodamine B (RhB). It can provide dual-emitting fluorescence that facilitates self-calibration in sensing. The RhB@Er-MOF has higher accuracy than the Er-MOF with regard to the fluorescence-selective and anti-interference performance of Fe³⁺ and quenching coefficient K_sv values of 1.97 × 10⁴ M⁻¹, which are attributed to its self-calibration function that can eliminate environmental interference. The fluorescence quenching mechanism was explained by our experiments and density functional theory (DFT) calculations. Furthermore, RhB@Er-MOF can achieve the visual and rapid selective detection of Fe³⁺ by a smartphone RGB color analysis application, resulting in the dual-signal output performance of the material. Full article

The latest research indicates that Large Vision-Language Models (VLMs) have a wide range of applications in the field of remote sensing. However, the vast amount of image data in this field presents a challenge in selecting high-quality multimodal data, which are essential for saving computational resources and time. Therefore, we propose an adaptive fine-tuning algorithm for multimodal large models. The core steps of this algorithm involve two stages of data truncation. First, the vast dataset is projected into a semantic vector space, where the MiniBatchKMeans algorithm is used for automated clustering. This classification ensures that the data within each cluster exhibit high semantic similarity. Next, the data within each cluster are processed by calculating the translational difference between the original and perturbed data in the multimodal large model’s vector space. This difference serves as a generalization metric for the data. Based on this metric, we select data with high generalization potential for training. We apply this algorithm to train the InternLM-XComposer2-VL-7B model on two 3090 GPUs, using one-third of the GeoChat multimodal remote sensing dataset. The results demonstrate that our algorithm outperforms state-of-the-art baselines. The model trained on our optimally chosen one-third dataset, as validated through experiments, showed only a 1% reduction in performance across various remote sensing metrics compared to the model trained on the full dataset. This approach significantly preserved general-purpose capabilities while reducing training time by 68.2%. Furthermore, the model achieved scores of 89.86 and 77.19 on the UCMerced and AID evaluation datasets, respectively, surpassing the GeoChat dataset by 5.43 and 5.16 points. It only showed a 0.91-point average decrease on the LRBEN evaluation dataset. Full article

Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. In this study, we developed the optimal tree height-diameter at breast height (DBH) growth models for major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. Then, the AGB of the GGP stands was estimated using the Sentinel-2 data and the gradient boosting decision tree (GBDT) algorithm. The results showed that the AGB sample dataset constructed using the proposed approach exhibited strong consistency with field-measured data (R² = 0.89). The GBDT-based AGB estimation model shows high accuracy, with an R² of 0.96 and a root mean square error (RMSE) of 560 g/m². Key variables such as tasseled cap greenness (TCG), red-edge normalized difference vegetation index (RENDVI), and visible-band difference vegetation index (VDVI) were identified as highly important. This highlights that vegetation indices and tasseled cap transformation index information are key factors in estimating AGB. The AGB of major tree species in the new round of the GGP stands in Inner Mongolia ranged from 120 to 9253 g/m², with mean values of 978 g/m² for poplar, 622 g/m² for Mongolian Scots pine, and 313 g/m² for Chinese red pine species. This study offers a practical method for AGB estimation in GGP stands, contributing significantly to sustainable forest management and ecological conservation efforts. Full article

Conventional Distributed Power Flow Controllers (DPFCs) rely on third-harmonic currents to facilitate active power exchange between the series side and the system, requiring specific Δ/YN and YN/Δ transformer configurations at branch terminals. This limitation restricts their application in distribution networks. To overcome these constraints, this paper proposes a Novel Distributed Power Flow Controller (NDPFC) topology specifically designed for distribution networks. This design eliminates the need for third-harmonic currents and specific transformer configurations, enhancing deployment flexibility. The paper first explains the NDPFC operating principles and verifies its power flow regulation capabilities through a typical distribution network system. Furthermore, we develop electromagnetic transient mathematical models for both series and shunt components of the NDPFC, proposing a triple-loop control strategy for Series-I and Series-II control methods to enhance system robustness and control precision. A systematic stability analysis confirms the proposed controller’s robustness under various operating conditions. Simulation results demonstrate that in various distribution network scenarios, the NDPFC effectively achieves comprehensive power flow regulation, compensates three-phase imbalances, and facilitates renewable energy integration, significantly improving distribution network power quality. A comparative analysis shows that the NDPFC achieves 15% faster response times and 12% lower losses compared to conventional power flow controllers. Full article

Structural health monitoring (SHM) systems are very useful for evaluating the performance of bridges in service. In this paper, the SHM system implemented on the Poyang Lake Second Bridge is investigated, and the monitored data are analyzed for performance evaluation, damage identification, and model updating of the bridge. First, the measured data are examined for environmental effects, structural behaviour, and modal identification. Based on the bridge construction information, a finite element (FE) model is constructed for the cable-stayed bridge. Subsequently, the regularized model updating approach is employed to calibrate the constructed numerical model by using the measured modal data. Several vibration-based methods for structural damage identification are proposed to inversely identify the simulated damage within the cable-stayed bridge using the test data. The results indicate that the measured structural responses, such as cable forces and bridge deck deflections, vary over time and highlight discrepancies in the initial FE model. This FE numerical model can then be effectively adjusted using the proposed model updating method, which enhances the connection between the real cable-stayed bridge and the modified FE numerical model. From the modal data, the simulated damage in the main structural members of the cable-stayed bridge can be correctly identified using the proposed methods. Full article

An innovative design of a hydrophone based on a piezoelectric composite film of $AlN/S{c}_{0.2}A{l}_{0.8}N$ is presented. By designing a non-uniform composite sensitive layer, the dielectric loss and defect density are significantly reduced, while the high-voltage electrical characteristics of scandium-doped aluminum nitride are retained. X-ray diffraction analysis shows that the sensitive films have excellent crystal quality (FWHM is 0.34°). According to the standard underwater acoustic calibration test, the device exhibits full directivity with a minimum deviation of ±0.5 dB at 1 kHz frequency, sound pressure sensitivity of −162.9 dB (re: 1 V/$\mathsf{\mu }$Pa) and equivalent noise density of 46.1 dB (re: 1 $\mathsf{\mu }$Pa/√Hz). The experimental results show that the comprehensive performance of the piezoelectric heterostructure hydrophone meets the standard of commercial high-end hydrophones while maintaining mechanical stability, and provides a new solution for underwater acoustic sensing. Full article

China is facing increasing marine microplastic pollution. Despite the fact that the South China Sea is the largest marine area in China, the ecological danger and present state of microplastic contamination in this region have not been systematically and comprehensively investigated. This study analyzed the abundance, distribution, and characteristics of microplastics in different environmental media and biological samples from the Xisha Islands in the South China Sea, and then the ecological risk assessment of microplastic pollution in this area was conducted. The findings indicated that the quantities of sediments, soil, water, fish, and birds were 41.56 ± 19.12 items/kg, 92.94 ± 111.05 items/kg, 2.89 ± 1.92 items/L, 2.57 ± 2.12 items/ind, and 1.702 ± 1.50 items/ind, respectively. By evaluating the pollution load index (PLI), polymer hazard index (PHI), and potential ecological risk index (PERI), the PLI of the Xisha Islands in the South China Sea as a whole indicated that the hazard level was slightly polluted, the PHI was at a high-risk level, and the PERI samples were at no risk, except for the soil and seawater, which were at a medium-risk level. Full article

Words in environmental print are exposed to young children before formally learning to read, and attention to these words is linked to their reading ability. Inversion sensitivity, the ability to distinguish between upright and inverted words, is a pivotal milestone in reading development. To further explore the relationship between attention to words in environmental print and early reading development, we examined whether children with varying reading abilities differed in inversion sensitivity to these words. Participants included children with low (18, 8 males, 5.06 years) and high (19, 10 males, 5.00 years) reading levels. Using an eye-tracking technique, we compared children’s attention to upright and inverted words in environmental print and ordinary words during a free-viewing task. In terms of the percentage of fixation duration and fixation count, results showed that children with high reading abilities exhibited inversion sensitivity to words in environmental print, whereas children with low reading abilities did not. Unexpectedly, in terms of first fixation latency, children with low reading abilities showed inversion sensitivity to ordinary words, while children with high reading abilities did not. These findings suggest that inversion sensitivity to words in environmental print is closely linked to early reading ability. Full article

Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such conditions. To improve the model’s ability to extract image features, DoubleNet incorporates several key innovations, such as a unique multi-head self-attention mechanism (Fused-MHSA), a modified activation function (SA-GELU), and a specialized operation block (DNBLK). Based on them, DoubleNet is structured as an encoder–decoder network that includes two parallel subnetworks: one dedicated to processing 2D feature maps and the other focused on 1D tensors. These subnetworks interact through two feature fusion networks, which operate in both the encoder and decoder stages, facilitating a more integrated feature extraction process. Additionally, we utilized a specially annotated dataset comprising images fused with RGB and mask, with five navigation points marked to enhance the accuracy of point localization. As a result of these innovations, DoubleNet achieves a remarkable 95.75% percentage of correct key points (PCK) and operates at 71.16 FPS on our dataset, with a combined performance that outperformed several well-known key point detection algorithms. DoubleNet demonstrates strong potential as a competitive solution for generating effective navigation routes for robots operating in vineyards with unstructured roads. Full article

Australia submitted its updated nationally determined contribution (NDC) in 2022 and increased the ambition of its 2030 target, committing to reduce greenhouse gas (GHG) emissions 43% below 2005 levels by 2030. A new set of policies was proposed in its NDC, but the potential effectiveness of these policies should be assessed. This research has applied an environmentally extended input–output analysis combined with linear programming and set six types of scenarios to assess the maximum GHG emission reductions in 2030. The six scenarios include “business as usual”, different levels of sector-differentiated growth, a low-carbon electricity mix, a reduction in the emissions intensity of the mining sector, an increase in the electricity efficiency of intermediate inputs, and the implementation of all measures. Results show that implementing all measures simultaneously can achieve Australia’s 2030 emission targets, with total emissions in 2030 being 317.62 Mt CO₂-e, which is a 39.08% reduction compared to the BAU. This study contributes to understanding changes in scenarios for the development of carbon emissions to achieve Australian NDCs. Full article

La-Co-doped ferrite is widely used due to its excellent magnetic properties, but the mechanisms of La-Co doping on its phase formation and magnetic properties remain unclear. This study clarifies the phase formation mechanisms and reveals that La-Co doping reduces the formation temperatures of the intermediate phase SrFeO_3−x and thus the final SrFe₁₂O₁₉ phase. This promotes complete formation of SrFe₁₂O₁₉, enhancing saturation magnetization. The unexpected change in coercivity after La-Co doping contradicts the variation in the determined magnetocrystalline anisotropy field. We identify that it arises from the La-Co doping lowering the formation temperature of SrFe₁₂O₁₉, leading to excessive particle growth. Full article