Search results

Fluorine materials have received the keen attention of many researchers because of their water repellency and low surface free energy. The purpose of this paper is to prepare self-crosslinking fluorocarbon polyacrylate latexes containing different fluorocarbon chain lengths by semi-continuous seeded emulsion polymerization technology.

Methyl methacrylate (MMA), butyl acrylate (BA), hydroxypropyl methacrylate (HPMA) and fluorine-containing monomers were used as main monomers. The fluorine-containing monomers included hexafluorobutyl methacrylate (HFMA), dodecafluoroheptyl methacrylate (DFMA) and trifluorooctyl methacrylate (TFMA). Potassium persulfate (KPS) was used as thermal decomposition initiator, non-ionic surfactant alkyl alcohol polyoxyethylene (25) ether (DNS-2500) and anionic surfactant sodium dodecylbenzene sulfonate (SDBS) as mixed emulsifier.

Through optimizing the reaction conditions, the uniform and stable latex is gained. The polymer of structure was characterized by Fourier transform infrared spectroscopy (FTIR). Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and contact angle (CA) were tested on latex films. The particle size and distribution range of emulsion were tested with nano particle size analyzer. After comprehensively comparing the latexes and films prepared by HFMA, DFMA and TFMA, the performance of DFMA monomer modified is better.

The self-crosslinking acrylic emulsion is prepared via semi-continuous seeded emulsion polymerization, which methyl methacrylate (MMA), butyl acrylate (BA), hydroxypropyl methacrylate (HPMA) and fluorine-containing monomers were used as main monomers. The fluorine-containing monomers were composed of hexafluorobutyl methacrylate (HFMA), dodecafluoroheptyl methacrylate (DFMA) and trifluorooctyl methacrylate (TFMA). Potassium persulfate (KPS) was used as thermal decomposition initiator, non-ionic surfactant alkyl alcohol polyoxyethylene (25) ether (DNS-2500) and anionic surfactant sodium dodecylbenzene sulfonate (SDBS) as mixed emulsifier. There are two main innovations. One is that the self-crosslinking acrylic emulsion is prepared successfully. The other is that the effects of monomers containing different fluorocarbon chain lengths on polyacrylate, such as monomer conversion rate, coagulation rate, mechanical stability, chemical stability, emulsion particle size and storage stability, are studied in detail.

Silane cross-linkers have been used to strengthen the mechanical stabilities and friction resistance of plastic products. Therefore, the effect of silane cross-linkers on latex has been studied through preparing modified self-cross-linking long fluorocarbon polyacrylate latex. In this paper, nonionic surfactant alcohol ether glycoside (AEG1000) and anionic polymerizable surfactant 1-allyloxy-3-(4-nonylphenol)-2-propanol polyoxyethylene (10) ether ammonium sulfate (DNS-86) acted as mixed emulsifier and 3-(methacryloyloxy) propyltrimethoxysilane (KH-570) and bis (2-ethylhexyl) maleate (DOM) were used as functional monomers.

The modified acrylate polymer latex was synthesized through the semi-continuous seeded emulsion polymerization with methyl methacrylate (MMA), butyl acrylate (BA), dodecafluoroheptyl methacrylate (DFMA) and hydroxypropyl methacrylate (HPMA) as main monomers. Potassium persulfate (KPS) was applied to initiate polymerization reaction, nonionic surfactant AEG1000 and DNS-86 acted as emulsifier, KH-570 and DOM were used as functional monomers, respectively.

The optimum conditions of synthesizing the modified latex were the following. The mass ratio of monomers containing MMA, BA, DFMA, HPMA, KH-570 and DOM was 13.58:13.58:0.90:1.20:0.15:0.60, the usage of initiator KPS was 0.5% of the total weight of monomers and the amount of emulsifier was 7% of all monomers with AEG1000:DNS-86 = 1:1. The results indicated that the conversion of monomer was 99% and the coagulation was about 2.0%.

The resultant latex was modified silane cross-linker KH-570 and DOM, which positively affected the comprehensive properties of latex and its film. Apart from this, the novel mixed emulsifier was used to improve the size and distribution of latex particles and reduce environmental problems caused by the use of emulsifiers.

The study aimed to analyze the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong Province.

A conceptual model of the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong province is established, which takes the business model as the mediating variable and political association as the moderating variable. Multivariate statistical analysis and the MacKinnon confidence interval method were used to analyze 418 questionnaires.

The results show that both relational embeddedness and structural embeddedness have significant positive effects on the innovation performance of high-tech enterprises in Guangdong Province. The business model has a partial mediating effect between relationship embeddedness, structure embeddedness, and innovation performance of high-tech enterprises in Guangdong Province, respectively. Political relevance has a significant negative moderating effect on the relationship between the relationship embeddedness and innovation performance of high-tech enterprises in Guangdong Province, but the moderating effect on structural embeddedness and innovation performance of high-tech enterprises in Guangdong province has not been verified.

The study of this paper also has some shortcomings: very few data research samples exist; the external factors affecting the performance of high-tech enterprises in Guangdong Province need to be further refined. The research scale needs further improvement.

In this paper, embedding theory, transaction cost theory, resource dependence theory, rent-seeking theory, new institution theory and uncertainty management theory were integrated by system attempt to reveal the mediating and moderating roles of business model and political relevance, respectively, between network embeddedness behavior and entrepreneurial innovation performance of high-tech enterprises. The research conclusions expand the relevant research in the field of entrepreneurial innovation. At the same time, the research results provide theoretical support and reference for the innovative growth of high-tech enterprises and government behavior decision-making in Guangdong province.

Network embeddedness will have a profound impact on the entrepreneurial innovation performance of high-tech enterprises. Existing research has overlooked discussing this issue from the perspective of internal and external influencing factors within the enterprise. Therefore, this study addresses this issue by (1) introducing the business model as the mediating variable from an internal perspective of the enterprise, (2) introducing political association as the moderating variable from an external perspective of the enterprise and (3) 418 original questionnaires of high-tech enterprises in Guangdong Province were used to test the effect of the study variables.

Whether automatically generated summaries of health social media can aid users in managing their diseases appropriately is an important question. The purpose of this paper is to introduce a novel text summarization approach for acquiring the most informative summaries from online patient posts accurately and effectively.

The data set regarding diabetes and HIV posts was, respectively, collected from two online disease forums. The proposed summarizer is based on the graph-based method to generate summaries by considering social network features, text sentiment and sentence features. Representative health-related summaries were identified and summarization performance as well as user judgments were analyzed.

The findings show that awarding sentences without using all the incorporating features decreases summarization performance compared with the classic summarization method and comparison approaches. The proposed summarizer significantly outperformed the comparison baseline.

This study contributes to the literature on health knowledge management by analyzing patients’ experiences and opinions through the health summarization model. The research additionally develops a new mindset to design abstractive summarization weighting schemes from the health user-generated content.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.

For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.

Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.

Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.