Search Results (3)

To ensure the safety of supercritical CO₂ pipelines and address the limitations of full-scale fracture tests, such as high risk and substantial investment, software for evaluating the crack arrest toughness of CO₂ pipelines containing impurities was developed based on the Battelle Two-Curve Model (BTCM) in this study. The software is programmed in Python (v.3.12.4), with a graphical user interface (GUI) built using PyQt6 (v.6.10.0) and a three-tier architecture design. It integrates the resistance curve model and the decompression wave model. To determine the thermodynamic state of the fluid, a large property database covering pure components and various mixtures is embedded, incorporating state equations such as PR, HEOS, and GERG-2008. The software can generate pressure drop curves, decompression curves, and resistance curves. The pressure plateau can be quickly identified by examining the pressure drop curve. Whether the pipeline can achieve self-crack arrest can be rapidly judged by comparing the positional relationships between the decompression curve and the resistance curve. To verify the accuracy of the software’s calculation results, comparisons were conducted with previous decompression wave experimental data, full-scale burst test data of a CO₂ pipeline, and the international HLP model. The calculation error of the software is within 10%. The development and application of this software provide a convenient, efficient, and accurate practical tool for the calculation of crack arrest toughness and crack arrest evaluation of supercritical CO₂ pipelines. Full article

Due to the explosive rise of multimodal content in online social communities, cross-modal learning is crucial for accurate fake news detection. However, current multimodal fake news detection techniques face challenges in extracting features from multiple modalities and fusing cross-modal information, failing to fully exploit the correlations and complementarities between different modalities. To address these issues, this paper proposes a fake news detection model based on a one-dimensional CCNet (1D-CCNet) attention mechanism, named BTCM. This method first utilizes BERT and BLIP-2 encoders to extract text and image features. Then, it employs the proposed 1D-CCNet attention mechanism module to process the input text and image sequences, enhancing the important aspects of the bimodal features. Meanwhile, this paper uses the pre-trained BLIP-2 model for object detection in images, generating image descriptions and augmenting text data to enhance the dataset. This operation aims to further strengthen the correlations between different modalities. Finally, this paper proposes a heterogeneous cross-feature fusion method (HCFFM) to integrate image and text features. Comparative experiments were conducted on three public datasets: Twitter, Weibo, and Gossipcop. The results show that the proposed model achieved excellent performance. Full article

The ability of dental materials to induce the mineralization of enamel like hydroxyapatite (HA) is of great importance. In this article, a novel kind of dental restorative material characterized by a mineralization ability was fabricated by photopolymerization. Calcium methacrylate (CMA) was introduced into the classical bisphenol A-glycidyl methacrylate (Bis-GMA) and triethylene glycol dimethacrylate (TEGDMA) dental resin formulation. This functional dental resin (BTCM) was calcium-rich and can be prepared simply by one-step photopolymerization. The influence of CMA on the photopolymerization kinetics, the dental resin’s mechanical properties, and its capacity to induce dynamic in situ HA mineralization were examined. Real-time FTIR, compression modulus, scanning electron microscopy, X-ray spectroscopy, MTT assay, and cell attachment test were carried out. The obtained data were analyzed for statistical significance using analysis of variance (ANOVA). Double bond conversion could be completed in less than 300 s, while the compression modulus of BTCM decreased with the increase in CMA content (30 wt%, 40 wt%, and 50 wt%). After being soaked in Ca(NO₃)₂ and Na₂HPO₄ solutions alternatively, dense HA crystals were found on the surface of the dental resin which contained CMA. The amount of HA increased with the increase in CMA content. The MTT results indicated that BTCM possesses good biocompatibility, while the cell adhesion and proliferation investigation demonstrated that L929 cells can adhere and proliferate well on the surface of BTM. Thus, our approach provides a straightforward, cost-effective, and environmentally friendly solution that has the potential for immediate clinical use. Full article