Publisher Correction: Copper adparticle enabled selective electrosynthesis of n-propanol.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Effect of Nano-Clay Dispersion on Pore Structure and Distribution of Hardened Cement Paste

Nano-clay has the potential to improve the properties of cement-based materials. However, the effectiveness of this improvement is influenced by the dispersion of the nano-clay. The effects of different nano-clay dispersion techniques on cement-based material properties and pore structure complexity were studied. The samples were prepared using manual and mechanical dispersion methods. The mechanical properties of the specimens were evaluated, and the pore characteristics of the cement-based materials were analysed using mercury intrusion porosimetry. The study investigated the effect of the dispersion method on the nano-clay dispersion. The complexity of the pore structure was evaluated using a fractal model, and the relationship between the fractal dimension, mechanical properties, and pore structure was analysed. The findings indicate that mechanical dispersion results in better dispersion than manual dispersion, and the mechanical properties of mechanical dispersion are superior to those of manual dispersion. Nano-clay particles can improve the internal pore structure of cement materials. Through mathematical calculation, the surface fractal dimension is between 2.90 and 2.95, with good fractal characteristics. There is a good correlation between the surface fractal dimension and the mechanical properties. The addition of nano-clay can reduce the complexity of the pore structure, and the fractal dimension has an excellent linear relationship with the pore structure

The Classification and Mechanism of Microcrack Homogenization Research in Cement Concrete Based on X-ray CT

Old cement pavement directly overlaid with an asphalt layer produces many reflection cracks. Using microcrack homogenization technology to treat old cement pavement can effectively reduce the occurrence of reflection cracks. Micro-crack homogenization is relatively mature at the technical level; however, there is relatively little research on its mechanism. To evaluate the microcrack effect of old cement pavement, we conducted core sampling on a project road section after the appearance of microcracks. The core samples were sliced by X-ray tomography (X-ray CT). The mesostructure of the core samples was obtained. The core sample was further divided into microscopic cracks and macroscopic cracks according to the length and width of the crack. The development characteristics of cracks were subdivided into type I-III microcracks and type I-IV cracks. The core-drilling sample was divided into 5652 CT images. The statistical results showed that there were 3582 type-I microcracks, 3197 type-II microcracks, and 1835 type-III microcracks. Among the specimens, the minor proportion of microcracks was 32.87%, the most significant proportion was 100.00%, and the average ratio was 47.51%. Furthermore, the cracks development law and formation mechanism were analyzed based on CT images and cracking statistics. The results showed: (1) The microcrack homogenization process produced many microcracks in the test section and achieved a specific microcrack effect. (2) The cracks produced by the microcrack homogenization process tended to develop along the transition zone of the aggregate–cement mortar interface. The development of microcracks was mainly related to the aggregates’ shape and gradation, as well as the energy required to generate cracks. The research conclusions of this paper can be used as a theoretical basis for the optimization and improvement of the microcrack homogenization process

The Classification and Mechanism of Microcrack Homogenization Research in Cement Concrete Based on X-ray CT

Old cement pavement directly overlaid with an asphalt layer produces many reflection cracks. Using microcrack homogenization technology to treat old cement pavement can effectively reduce the occurrence of reflection cracks. Micro-crack homogenization is relatively mature at the technical level; however, there is relatively little research on its mechanism. To evaluate the microcrack effect of old cement pavement, we conducted core sampling on a project road section after the appearance of microcracks. The core samples were sliced by X-ray tomography (X-ray CT). The mesostructure of the core samples was obtained. The core sample was further divided into microscopic cracks and macroscopic cracks according to the length and width of the crack. The development characteristics of cracks were subdivided into type I-III microcracks and type I-IV cracks. The core-drilling sample was divided into 5652 CT images. The statistical results showed that there were 3582 type-I microcracks, 3197 type-II microcracks, and 1835 type-III microcracks. Among the specimens, the minor proportion of microcracks was 32.87%, the most significant proportion was 100.00%, and the average ratio was 47.51%. Furthermore, the cracks development law and formation mechanism were analyzed based on CT images and cracking statistics. The results showed: (1) The microcrack homogenization process produced many microcracks in the test section and achieved a specific microcrack effect. (2) The cracks produced by the microcrack homogenization process tended to develop along the transition zone of the aggregate–cement mortar interface. The development of microcracks was mainly related to the aggregates’ shape and gradation, as well as the energy required to generate cracks. The research conclusions of this paper can be used as a theoretical basis for the optimization and improvement of the microcrack homogenization process

Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model

Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model

N-acetylcysteine attenuates the incidence of phlebitis induced by carbomer/vinorelbine gel

Background: The high incidence and severe clinical manifestations of phlebitis pose a complex and urgent clinical challenge. The rapid and simple establishment of animal phlebitis models and the development of preventive strategies are crucial to resolving this problem. Methods: In this study, we established such models by mixing vinorelbine ditartrate (VNR) and carbomer to form a sustained-release gel carrier, and then injected it around the veins rather than inside the vessels. Furthermore, we analyzed the efficacy of the carbomer/VNR gel in inducing phlebitis by monitoring the morphology of the veins using HE staining, immunohistochemical and immunofluorescence staining, and western blotting. Reactive oxygen species (ROS) and lipid peroxidation levels were determined using flow cytometry. Finally, we evaluated the inhibitory effect of N-acetylcysteine (NAC) on VNR-induced phlebitis in rabbits and rats. Results: Our findings suggested that the carbomer/VNR gel rapidly and easily induced phlebitis due to by retention of the gel in situ, wrapping the veins, and the prolonged release of VNR. NAC alleviated the VNR-induced oxidative stress response and expression of inflammatory cytokines by attenuating mitochondrial damage in venous endothelial cells, thereby preventing the occurrence of phlebitis in rabbits and rats. Conclusion: The in situ carbomer/VNR gel provides a rapid and simple method for establishing an animal model to study the pathogenesis of phlebitis. Furthermore, the observed therapeutic effect of NAC highlights its novel and efficacious role in preventing and treating phlebitis