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With the rapid economic growth and accelerated urbanization, solid waste recycling has become a critical priority for eco-friendly urban development. Although some studies have investigated cold-bonded aggregates using MSWIFA, the effect of curing environments on heavy metal immobilization and mechanical behavior remains poorly understood. This study utilizes solid wastes such as municipal solid waste incineration fly ash (MSWIFA) and carbide slag to produce environmentally friendly non-sintered lightweight aggregates, and the effect of curing temperature and solution on the properties, reaction products as well as heavy metal leaching was investigated. The results show that under steam curing at 60 °C for 12 h, using calcium carbide slag supernatant as the granulation solution, the mechanical strength of the aggregates reached more than 7.02 MPa, meeting the Chinese standards for lightweight aggregates. Steam curing promotes the formation of hydrocalumite minerals, but the peak gradually weakens when the curing temperature reaches 75 °C. Microstructural analysis through XRD, FTIR, SEM, and XPS revealed that high temperatures activated the latent reactivity of slag, accelerating the reactions and promoting the generation of more C-S-H gel and hydrated calcium aluminates. This study proposes an innovative use of MSWIFA and carbide slag to produce non-sintered aggregates and systematically evaluates their performance under various curing conditions. The results are validated by detailed characterization, confirming both the originality and reliability of the work. In conclusion, the non-sintered lightweight aggregates prepared from MSWIFA demonstrate good mechanical performance and environmental adaptability, providing a sustainable alternative to natural aggregates and a useful reference for eco-friendly construction materials.
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http://dx.doi.org/10.1016/j.envres.2025.122180 | DOI Listing |
Med Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China.
Surgical instrument segmentation plays an important role in robotic autonomous surgical navigation systems as it can accurately locate surgical instruments and estimate their posture, which helps surgeons understand the position and orientation of the instruments. However, there are still some problems affecting segmentation accuracy, like insufficient attention to the edges and center of surgical instruments, insufficient usage of low-level feature details, etc. To address these issues, a lightweight network for surgical instrument segmentation in gastrointestinal (GI) endoscopy (GESur_Net) is proposed.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Department of Orthopaedics, The First Affiliated Hospital of Soochow University, Suzhou, China.
Introduction: CT-based classification of distal ulnar-radius fractures requires precise detection of subtle features for surgical planning, yet existing methods struggle to balance accuracy with clinical efficiency. This study aims to develop a lightweight architecture that achieves accurate AO (Arbeitsgemeinschaft für Osteosynthesefragen) typing[an internationally recognized fracture classification system based on fracture location, degree of joint surface involvement, and comminution, divided into three major categories: A (extra-articular), B (partially intra-articular), and C (completely intra-articular)] while maintaining real-time performance. In this task, the major challenges are capturing complex fracture morphologies without compromising detection speed and ensuring precise identification of small articular fragments critical for surgical decision-making.
View Article and Find Full Text PDFFront Plant Sci
August 2025
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Deep learning models for rice pest detection often face performance degradation in real-world field environments due to complex backgrounds and limited computational resources. Existing approaches suffer from two critical limitations: (1) inadequate feature representation under occlusion and scale variations, and (2) excessive computational costs for edge deployment. To overcome these limitations, this paper introduces GhostConv+CA-YOLOv8n, a lightweight object detection framework was proposed, which incorporates several innovative features: GhostConv replaces standard convolutional operations with computationally efficient ghost modules in the YOLOv8n's backbone structure, reducing parameters by 40,458 while maintaining feature richness; a Context Aggregation (CA) module is applied after the large and medium-sized feature maps were output by the YOLOv8n's neck structure.
View Article and Find Full Text PDFMar Pollut Bull
August 2025
College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China. Electronic address:
As underwater ecosystems face escalating threats from increasing anthropogenic debris, autonomous monitoring and removal have become critical. Here we present LCSA-DETR, a lightweight object detection model optimized for deployment on resource-constrained autonomous underwater vehicles (AUVs). Built upon RT-DETR, LCSA-DETR introduces four core modifications to improve efficiency and detection performance: (i) replacement of the ResNet-18 backbone with StarNet to reduce computational complexity; (ii) a lightweight cross-stage aggregation encoder (LC-Encoder) with skip connections and feature alignment to enhance parameter efficiency; (iii) an adaptive kernel fusion block (AKFB) for improved multi-scale feature representation; and (iv) a bidirectional feature pyramid network (BiFPN) with dynamic weighting to enable effective feature fusion.
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