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The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption.

The evaluation and prediction of dam concrete quality have important theoretical significance for preventing catastrophes in concrete dams and predicting their long-term safety characteristics. To realize a comprehensive index for the evaluation and prediction of dam concrete characteristics, the fractal characteristics of dam concrete were analyzed using elastic-wave computed tomography (EWCT). The dam concrete quality evaluation model was established by combining the defined EWCT fractal dimensions with the strength and maturity of the dam concrete. Furthermore, a dam concrete artificial neural network (ANN) prediction model with a multi-index input layer was developed. The results indicated that the EWCT images exhibited the early evolution and distribution characteristics of dam concrete specimens, and there was a strong positive correlation between the EWCT fractal dimension and the age of the dam concrete. The grey relational analysis coefficient between the EWCT fractal dimension and fully graded concrete strength was similar to the coefficient between the wet-screened and fully graded concretes. Among the five dam concrete detection indices used in the ANN, the weight contribution rate of the EWCT fractal dimension to the dam concrete ANN prediction model was 23.15 %. In conclusion, the fractal dimension may be used as a critical index in dam concrete detection to pave the way for developing intelligent dam concrete detection and monitoring based on non-destructive technology.

The fundamental idea behind few-shot learning is to employ sparse labeled data to effectively handle novel tasks, whereas most existing mainstream approaches mostly rely on prior experience gained from previous situations. Nonetheless, effective knowledge transfer is sometimes hampered by constraints on new samples and barriers between classes. To address these issues, this research presents a novel background-filtering feature-enhanced graph network (BFFE-GNN) that attempts to generate relationships between graphs in order to explicitly describe and transmit inter-class relationships. To specifically address the issue of inadequate information utilization brought on by sample background interference, which is frequent in classification tasks, we employ a novel background-filtering feature-enhanced graph network. Effective data information extraction from complicated datasets is challenging due to the original module’s relatively simple network structure during the feature extraction stage and the interference of the image background. The background-filtering module was specifically introduced, which enhances spatial attention. This not only improves the quality of feature extraction but also effectively lessens the influence of picture background on classification results. In addition, we have improved the background-filtering-based feature gap calculation by implementing a feature-enhanced module. To demonstrate the adaptability of the BFFE-GNN model, we not only ran experiments on two publicly available datasets, MiniImagenet and TiredImagenet, but we also created our own Tool dataset. The method’s exceptional performance and universal applicability in the field of few-shot picture classification are clearly demonstrated by the experimental findings, which indicate that it greatly outperforms the majority of existing similar methods. This discovery establishes a strong basis for further research in the field of few-shot learning in addition to offering fresh insights into the subject.

Graph neural networks (GNNs) perform well in text analysis tasks. Their unique structure allows them to capture complex patterns and dependencies in text, making them ideal for processing natural language tasks. At the same time, XGBoost (version 1.6.2.) outperforms other machine learning methods on heterogeneous tabular data. However, traditional graph neural networks mainly study isomorphic and sparse data features. Therefore, when dealing with tabular data, traditional graph neural networks encounter challenges such as data structure mismatch, feature selection, and processing difficulties. To solve these problems, we propose a novel architecture, XGNN, which combines the advantages of XGBoost and GNNs to deal with heterogeneous features and graph structures. In this paper, we use GAT for our graph neural network model. We can train XGBoost and GNN end-to-end to fit and adjust the new tree in XGBoost based on the gradient information from the GNN. Extensive experiments on node prediction and node classification tasks demonstrate that the performance of our proposed new model is significantly improved for both prediction and classification tasks and performs particularly well on heterogeneous tabular data.

Concrete structures in atmospheric environments are constantly affected by acid rain and carbon dioxide erosion, and the evolution of concrete neutralization is an important factor influencing the long-term performance of the material. To deepen our understanding of the long-term performance evolution mechanisms of concrete, this study reviews the mechanism of acid rain and carbonation neutralization, summarizes studies regarding the evolution of the macroscopic properties and microstructures of concrete, and discusses the internal and external influencing factors and prediction models for concrete neutralization evolution. A thorough analysis of existing studies reveals that the microscopic properties and microstructures of concrete undergo a process of optimization followed by decay in response to acid rain neutralization. The expansion products from acid rain can lead to the deterioration of concrete microstructure, whereas carbonation neutralization products enhance microstructural compactness. Moreover, the study found that the degree of concrete neutralization increases with an increase in the water-cement ratio (W/C). Furthermore, the maximal degree of concrete neutralization occurs within a relative humidity ranging from 45% to 65%. According to the diffusion theory, the process of concrete neutralization is an evolutionary one that operates from the outside to the inside of the material and is influenced by various factors. The aim of this review is to provide scholars with a systematic understanding of the evolution of concrete neutralization under acid rain and carbonation, and to establish a theoretical foundation for future research in this area. Potential areas for future research on concrete neutralization are also described in this review.

According to surveys, one in seven people in China are involved in stock trading, and the role of stocks in the global economy is growing. In terms of the entire stock market, the Chinese market alone has over 4000 stocks. Faced with a chaotic and diverse assortment of stocks, it is necessary to categorize them. On the one hand, it can facilitate stock market research, and on the other, it can make it easier for stockholders to purchase shares. The graph convolutional neural network-based SK-GCN model developed in this paper delivers excellent results in the categorization of stock classes. This model employs two layers of convolutional layers and activation functions to effectively categorize stocks by incorporating external nodes to expand stock features and drawing inspiration from short text classification. This strategy is highly innovative and produces promising outcomes. In this paper, we constructed the dataset by crawling the information of all stocks listed on the GEM of Oriental Fortune website. We achieved an accuracy of 83.04% and a macro-F1 value of 0.8303 under the assumption of small sample training, and its classification effect is significantly superior to other classification models.

As a result of the integration of the stock industry into the entire international economic system, stock companies publish hundreds of prospectuses every second. The ability to quickly and accurately classify the companies and categories to which these data belong, as well as improve the performance of the economic system, has become the key to unlocking its corresponding value at this stage. In order to solve this problem, graph neural network techniques are used to accomplish the classification of stocks in the science and technology version, thus indirectly alleviating the enormous pressure on the economic system. In this study, to complete semi-supervised classification, we propose the PA-GCN model, which takes the lead in graph attention calculation of stock nodes and introduces the Elu activation function. Specifically, in this study, we constructed a stock dataset and implemented a dropout layer to prevent overfitting. The final results on the stock dataset and the publicly accessible Cora dataset demonstrate that this strategy may effectively improve the economic system and that the model has good performance. In the two dataset tests, classification accuracy can be attained at 81.69% and 81.2%, respectively. It also demonstrates the method’s viability for classifying stock nodes.

In spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is presented with the objective of enhancing the accuracy of few-shot image classification. This task entails the classification of unlabeled query samples based on a limited set of labeled support examples. Specifically, the integration of the edge-conditioned graph neural network (EGNN) framework with hierarchical node residual connections is proposed. The primary aim is to enhance the performance of graph neural networks when applied to few-shot classification, a rather unconventional application of hierarchical node residual structures in few-shot image classification tasks. It is noteworthy that this work represents an innovative attempt to combine these two techniques. Extensive experimental findings on publicly available datasets demonstrate that the methodology surpasses the original EGNN algorithm, achieving a maximum improvement of 2.7%. Particularly significant is the performance gain observed on our custom-built dataset, CBAC (Car Brand Appearance Classification), which consistently outperforms the original method, reaching an impressive peak improvement of 11.14%.

In order to hide image information more efficaciously, improve the radio of workpiece and quality of encryption, this paper proposes delayed chaotic neuron dynamical system, extracts a single neuron research model to determine its dynamic properties, analyzes the model’s sensitivity, stability and the randomness of the chaotic sequence, and verifies its dynamics feasibility of combining the system with DNA encoding. The single delay neuron can simulate the unknown dynamic behavior and capability in the model in a certain sense which can effectively improve optimization ability and con-vergence speed of the neural to make the network more practical. DNA encoding also offers new train of thoughts and directions for the study of cryptography. In addition, by introducing the functional form of fuzzy number can further enhance the stability and adaptability of chaotic system in un-certain environment, and get more distinct chaotic phenomena. The experimental simulation and comprehensive data analysis of the algorithm are carried out in this paper. The data manifest that the system has a large chaotic range, can pass the randomness test of NIST and TestU01, has high complexity and sensitivity which could effectively avoid noise attacks. The encrypted information entropy of classical images can reach 7.9998, which confirms that it can effectively and safely realize the encryption of digital images.

This study combines the present stage of the node classification problem with the fact that there is frequent noise in the graph structure of the graph convolution calculation, which can lead to the omission of some of the actual edge relations between nodes and the appearance of numerous isolated nodes. In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the method of external referencing of similar word nodes, combined with Graph Convolutional Neural Network (GCN), Graph Attention Network (GAT), and the early stop algorithm. In order to demonstrate the applicability of the model, this paper employs two distinct types of node datasets for its investigations. The first is the Cora dataset, which is widely used in node classification at this time, and the second is a small-sample Stock dataset created by Eastern Fortune’s stock prospectus of the Science and Technology Board (STB). The experimental results demonstrate that the ENode-GAT model proposed in this paper obtains 85.1% classification accuracy on the Cora dataset and 85.3% classification accuracy on the Stock dataset, with certain classification advantages. It also verifies the future applicability of the model to the fields of stock classification, tender document classification, news classification, and government announcement classification, among others.

Noise is ubiquitous in the real-world environment. At present, most scholars only include the stage of Gaussian white noise when applying noise in neural networks and regard white noise as a tool to optimize the network model, which is far from enough, because noise not only affects the optimization ability of the Hopfield neural network but can also better fit the needs of the actual use of the scene. Therefore, according to the problems in the existing research, a method is proposed to combine the neural network with colored noise according to the signal-to-noise ratio. Taking blue noise as an example, the anti-interference ability of the Hopfield neural network regarding colored noise is studied. The results show that for the Hopfield neural network driven by blue noise, by adjusting the neural network step size, excitation function and signal-to-noise ratio, it not only provides ideas for adding colored noise to the neural network but also enables the neural network model to have better optimization-seeking ability. The research results have some reference significance for improving the practical application of neural networks in noisy environments.

Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

In this paper, the ultimately exponentially bounded estimate problem of nonlinear stochastic discrete−time systems under generalized Lipschitz conditions is considered. A new sufficient condition making the estimation error system uniformly exponentially bounded in the mean square sense is given. The gain matrix can be obtained by solving matrix inequality. In the last section, numerical examples are provided verify the effectiveness of the conclusions.

Blockchain technology has brought about profound revolutions in supply chain management. Notably, in the agricultural sector, blockchain-based traceability has become an essential tool to maintain the safety and quality of farm commodities. However, the implementation of blockchain technology in agricultural traceability is not prevalent. In this paper, mathematical modeling and simulation methods were used to investigate the decision making regarding the adoption of blockchain traceability in agriculture, which comprises producers, processors, and governments. This paper provides further analysis of the optimal blockchain-based traceability strategies of the members of the agricultural product supply chain in different scenarios. The results reveal the following: (1) Producers and processors should manage the traceability costs for adopting blockchains to improve their brand image and gain more benefits. (2) The government should encourage supply chain agents to participate in traceability by establishing an effective reward-and-punishment mechanism. In addition, the research will help agricultural supply chain agents to design strategies to implement traceability in agriculture and create a transparent and efficient data-driven agricultural products supply chain. Furthermore, these findings provide guidance to policymakers to develop policies to accelerate the implementation of blockchain-based traceability systems to guarantee fraud-free and sustainable agricultural supply chains.

Carbon nanotubes (CNTs) are considered among the ideal modifiers for cement-based materials. This is because CNTs can be used as a microfiber to compensate for the insufficient toughness of the cement matrix. However, the full dispersion of CNTs in cement paste is difficult to achieve, and the strength of cement material can be severely degraded by the high air-entraining property of CNT dispersion. To analyze the relationship between the gas entrainment by CNT dispersion and mortar strength, this study employed data obtained from strength and micropore structure tests of CNT dispersion-modified mortar. The fractal dimensions of the pore volume and pore surface, as well as the box-counting dimension of the pore structure, were determined according to the box-counting dimension method and Menger sponge model. The relationship between the fractal dimensions of the pore structure and mortar strength was investigated by gray correlation. The results showed that the complexity of the pore structure could be accurately reflected by fractal dimensions. The porosity values of mortar with 0.05% and 0.5% CNT content were 15.5% and 43.26%, respectively. Moreover, the gray correlation between the fractal dimension of the pore structure and strength of the CNT dispersion-modified mortar exceeded 0.95. This indicated that the pore volume distribution, roughness, and irregularity of the pore inner surface were the primary factors influencing the strength of CNT dispersion-modified mortar.

There are many factors affecting the quality and safety of agricultural products in the supply chain of agricultural products. In order to ensure the quality and safety of agricultural products, suppliers and processors need to take their own quality measures to ensure the quality of agricultural products. Quality inspection departments need to strictly supervise suppliers and processors to ensure the implementation of quality measures by both parties. Within the supply chain, the decisions of these three stakeholders are affected by the initial intention, the cost of quality measures, and the penalty amount of the quality inspection department. Outside the supply chain, they are affected by government regulation and consumer feedback. This paper takes the stakeholders in the agricultural product supply chain as the object, brings suppliers, processors, and quality inspection departments into the evolutionary game model, brings the factors that affect the decision-making of these three stakeholders into the model as parameters to analyze the stability of the model in different situations, and then analyzes the factors that affect the decision-making of stakeholders through mathematical simulation according to specific examples. The results show that the enthusiasm of stakeholders to ensure the quality of agricultural products is most affected by the initial intention of each other and the cost of quality measures. At the same time, the punishment of the quality inspection department, the feedback of consumers, and the supervision of the government also play a good role in promoting quality.

The deterioration of concrete microstructures in freeze–thaw (F–T) cycles is the primary reason for the reduction in the service life of concrete. This paper reviews recent progress in the theory of damage mechanisms and damage models of concrete in F–T cycles. It is a detailed review of the salt-freeze coupling condition, microstructure testing, and models for the evolution of concrete properties that are subjected to F–T damage. Summarized in this paper are the deterioration theory of water phase transition; the mechanism of chloride-F–T and sulfate-F–T damage; the microstructure testing of hydration products, pore structure, microcracks, and interfacial transition zones (ITZ). Furthermore, F–T damage models for the macrostructure are presented. Finally, the issues that are existing in the research and outlook of concrete F–T damage are highlighted and discussed. This paper is helpful in understanding the evolution of F–T damage, and also provides a comprehensive insight into possible future challenges for the sustainable design and specifications of concrete in cold environments.