Effect of Different Water-Binder Ratios and Fiber Contents on the Fluidity and Mechanical Properties of PVA-ECC Materials

With the development of fiber-reinforced cement composites, the diversity and complexity of application scenarios require enhanced strength and ductility and tough materials in practical engineering. To explore the effects of different water-binder ratios and fiber contents on the fluidity, bending resistance, tensile properties, fracture toughness, and fracture behavior of polyvinyl alcohol (PVA) fiber cement composites, several groups of high ductility test blocks (PVA-engineering cementitious composites (ECC)) with different mixing ratios were designed in this study. Based on the expansion degree, the mechanical experimental data, and the electron microscopy scanning image results, K-value analysis was performed on the strain hardening strength criterion. The effect of the water–binder ratio and the fiber dosing on the PVA-ECC material was determined. Results show that the greater the water-binder ratio is, the better the fluidity of the ECC matrix is. In the same cement system and at the same water-binder ratio, the fluidity of the ECC paste gradually deteriorates with the increase of the fiber content. The water-binder ratio significantly affects the flexural tensile strength of the composite. The flexural and tensile strengths of the PVA-ECC gradually increase as the water-binder ratio decreases, but the ductility gradually decreases. The water-binder ratio of the substrate directly influences the damage behavior of the fibers within the substrate. With the gradual increase of the water-binder ratio, the fiber at the crack interface gradually changes from pull-out morphology to fracture morphology. The strain capacity and the multi-crack cracking performance decrease. To achieve improved working performance in the actual project, the matrix water-binder ratio should be controlled at approximately 0.45, and the PVA fiber dose of 1.7% is optimal. This study can provide a good reference for the optimization of practical engineering components

A Class of Unbounded Fourier Multipliers on the Unit Complex Ball

We introduce a class of Fourier multiplier operators Mb on n-complex unit sphere, where the symbol b∈Hs(Sω). We obtained the Sobolev boundedness of Mb. Our result implies that the operators Mb take a role of fractional differential operators on ∂

Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information

Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models

Research on Medical Text Classification Based on Improved Capsule Network

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models

Research on Medical Text Classification Based on Improved Capsule Network

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models

Research on Chinese Medical Entity Recognition Based on Multi-Neural Network Fusion and Improved Tri-Training Algorithm

Chinese medical texts contain a large number of medically named entities. Automatic recognition of these medical entities from medical texts is the key to developing medical informatics. In the field of Chinese medical information extraction, annotated Chinese medical text data are very few. In the named entity recognition task, there is insufficient labeled data, which leads to low model recognition performance. Therefore, this paper proposes a Chinese medical entity recognition model based on multi-neural network fusion and the improved Tri-Training algorithm. The model performs semi-supervised learning by improving the Tri-Training algorithm. According to the characteristics of the medical entity recognition task and medical data, the method in this paper is improved in terms of the division of the initial sub-training set, the construction of the base classifier, and the integration of the learning voting method. In addition, this paper also proposes a multi-neural network fusion entity recognition model for base classifier construction. The model learns feature information jointly by combining Iterated Dilated Convolutional Neural Network (IDCNN) and BiLSTM. Through experimental verification, the model proposed in this paper outperforms other models and improves the performance of the Chinese medical entity recognition model by incorporating and improving the semi-supervised learning algorithm

Effect of Different Water-Binder Ratios and Fiber Contents on the Fluidity and Mechanical Properties of PVA-ECC Materials

With the development of fiber-reinforced cement composites, the diversity and complexity of application scenarios require enhanced strength and ductility and tough materials in practical engineering. To explore the effects of different water-binder ratios and fiber contents on the fluidity, bending resistance, tensile properties, fracture toughness, and fracture behavior of polyvinyl alcohol (PVA) fiber cement composites, several groups of high ductility test blocks (PVA-engineering cementitious composites (ECC)) with different mixing ratios were designed in this study. Based on the expansion degree, the mechanical experimental data, and the electron microscopy scanning image results, K-value analysis was performed on the strain hardening strength criterion. The effect of the water–binder ratio and the fiber dosing on the PVA-ECC material was determined. Results show that the greater the water-binder ratio is, the better the fluidity of the ECC matrix is. In the same cement system and at the same water-binder ratio, the fluidity of the ECC paste gradually deteriorates with the increase of the fiber content. The water-binder ratio significantly affects the flexural tensile strength of the composite. The flexural and tensile strengths of the PVA-ECC gradually increase as the water-binder ratio decreases, but the ductility gradually decreases. The water-binder ratio of the substrate directly influences the damage behavior of the fibers within the substrate. With the gradual increase of the water-binder ratio, the fiber at the crack interface gradually changes from pull-out morphology to fracture morphology. The strain capacity and the multi-crack cracking performance decrease. To achieve improved working performance in the actual project, the matrix water-binder ratio should be controlled at approximately 0.45, and the PVA fiber dose of 1.7% is optimal. This study can provide a good reference for the optimization of practical engineering components

Spatial Distribution and Influencing Factors of Traditional Villages in Inner Mongolia Autonomous Region

This paper takes 207 traditional villages in Inner Mongolia as the research object and uses the ArcGIS10.7 software platform, using the nearest neighbor index, coefficient of variation analysis, spatial autocorrelation analysis, imbalance index, kernel density estimation method, geographical detector, and other methods to explore the spatial distribution characteristics and influencing factors of traditional villages in Inner Mongolia. The research shows that: (1) the spatial distribution of traditional villages in Inner Mongolia is condensed; the distribution of cities is uneven; and the overall distribution pattern of ‘two main and two vice’ is presented. (2) The traditional villages are mainly distributed in the altitude area of 500–1500 m, and their spatial distribution characteristics are positively correlated with the annual average temperature, annual precipitation, total population, the proportion of the primary industry, and the number of intangible cultural heritage, and negatively correlated with the slope, river distance, highway density, per capita GDP, urbanization, and the proportion of the secondary industry. (3) The results of GeoDetector2018 software show that socio-economic factors are the primary factors affecting the spatial distribution of traditional villages in Inner Mongolia, followed by natural geographical factors. The interaction and synergy between the influencing factors have increased significantly, which jointly affects the spatial pattern of the distribution of traditional villages in Inner Mongolia. The purpose of the study is to provide reference for the protection and development of traditional villages in Inner Mongolia and the implementation of the national rural revitalization strategy

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%