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In order to reduce the cement consumption during the production of autoclaved aerated concrete (AAC) made from circulating fluidized bed ash (CFBA), this paper investigated the effects of lime replacements for cement on gas-forming volume, bulk density, water absorption and compressive strength of AAC. The results show that the volume of gas formed in AAC during the pre-curing stage decreased as the lime content increased from 3 wt% to 22 wt%. After autoclaving cured for 7 h, the bulk density and water absorption of AAC increased with increasing the lime content. The compressive strength of AAC gradually increased from 3.0 MPa to 3.6 MPa as the lime content increased to 16 wt%. More defined tobermorite crystals formed as the lime content increases to 19 wt%, contributing much to the strength enhancement of AAC, whereas the lime content beyond 16 wt% adversely affected the microstructure of AAC.

Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis. This approach not only considers different data distributions but also incorporates neighborhood relationships, enhancing anomaly detection accuracy. First, by utilizing the adversarial process of GANs, including the loss function and the rarity of anomaly objects, we constrain the generator to primarily fit the probability distribution of normal objects during the unsupervised training process; Subsequently, a synthetic dataset is sampled from the generator, and the local synthetic density, which is defined by measuring the inverse of the sum of distances between a data point and all objects in its synthetic neighborhood, is calculated; Finally, the objects that show substantial density deviations from the synthetic data are classified as anomaly objects. Extensive experiments on seven real-world datasets from various domains, including medical diagnostics, industrial monitoring, and material analysis, were conducted using seven state-of-the-art anomaly detection methods as benchmarks. The GALD method achieved an average AUC of 0.874 and an accuracy of 94.34%, outperforming the second-best method by 7.2% and 6%, respectively. [ABSTRACT FROM AUTHOR]

Anomaly detection is crucial for the stable operation of mechanical systems, securing financial transactions, and ensuring network security, among other critical areas. Presently, Generative Adversarial Networks (GANs)-based anomaly detection methods either require labeled data for semi-supervised learning or face challenges with low computational efficiency and poor generalization when dealing with complex distributions. Aim to address these limitations, we introduce a generative adversarial synthetic neighbors-based unsupervised anomaly detection (GASN) method. This method integrates generative adversarial networks and neighborhood analysis techniques, enhancing anomaly detection performance through a two-stage detection process. In the first stage, the generative adversarial networks are trained on original dataset that containing a small number of anomaly objects. To minimize errors, the generator focuses on modeling majority object distributions, thus mapping noise to synthetic data resembling normal objects. In the second stage, GASN employs neighborhood analysis techniques to compare the similarity between original and synthetic data, assigning an anomaly factor to each object. This approach allows GASN to sensitively detect subtle anomaly objects. Extensive experiments conducted on twelve public datasets with five state-of-the-art methods demonstrate that the proposed method improves the AUC by 9.93% over the second-best method, proving its effectiveness in anomaly detection. [ABSTRACT FROM AUTHOR]

The synergistic effect of CNT and three-dimensional N-doped graphene foam (3DNG) on improving corrosion resistance of zinc-reinforced epoxy (ZRE) composite coatings was studied in this work. Although CNT itself was demonstrated to be effective to promote the anti-corrosion performance of the ZRE coating, the incorporation of additional 3DNG leads to further enhancement of its corrosion resistance under the synergistic effect of the hybrid carbon nanofillers with different dimensions. Both the content of the carbonaceous fillers and the ratio between them affected the performance of the coating. The optimal content of hybrid filler in the coating was determined to be only 0.1% with 3DNG:CNT = 1:3. With the modification of hybrid fillers, the corrosion current of the coating could be reduced by more than six orders of magnitude. Additionally, the immersion test of the pre-scratched coating directly demonstrated the evident contribution of the hybrid fillers to the sacrificial anode-based surface protection mechanism of the coating. These results confirmed the synergistic effect of the hybrid 1D and 3D carbonaceous fillers on promoting the corrosion inhibition of their coating, which could be promising for application in other functional composites. [ABSTRACT FROM AUTHOR]

Hybrid glass/carbon fiber (GCF) reinforced polymer composites are one of advanced composites with reduced cost, desired mechanical properties and special electrification performance. In the work, surface‐modified polypropylene (PP) non‐woven fabric was used as the interleaf for toughening these composites. The effects of the surface‐sulfonated PP fabric (PP/S) interleaves on various mechanical properties of the GCF reinforced epoxy (GCF/EP) composites were investigated firstly, including the mode II interlaminar fracture toughness and flexural strength. Comparing to the blank control sample, the GIIC of the hybrid composites interleaved with the sulfonated PP fabric was increased evidently, as well as its flexural strength. These demonstrate the great interleaving effect of PP/S fabric on the GCF/EP laminated composites. Moreover, the heterogeneous electrically conductive behavior and electro‐thermal effect of GCF/EP interleaved with and without PP/S were studied. Interestingly, comparing to the control sample, the conductivity of the interleaved GCF/EP composites increased in both two in‐plane direction. Due to the hybrid character of the plain woven GCF fabric, both the temperature increase rate and the equilibrium temperature of the GCF/EP composites were much larger than that of pure carbon fabric reinforced composites under the same electrification condition. Highlights: Polypropylene fabric grafted with sulfonate group (PP/S) was fabricated.The tensile property of fabric almost unchanged after the sulfonation reaction.PP/S was more effective in toughening glass/carbon fiber/epoxy composite (GCF).PP/S increased the conductivity in in‐plane directions of the hybrid composites.GCF/EP displayed superior electro heating effect to CF/EP. [ABSTRACT FROM AUTHOR]