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As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.

In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.

With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.

This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.

The purpose of this paper is to examine the performance of a fastener composite coating system, sherardized (SD) coating/zinc-aluminum (ZA) coating whether it has good performance in marine environment.

In this paper, SD coating was fabricated on fastener surface by solid-diffusion method. ZA coating was fabricated by thermal sintering method. Corrosion behaviours of the composite coating were investigated with potentiodynamic polarization curves, open circuit potential and electrochemical impedance spectroscopy methods.

Neutral salt spray (NSS) and deep sea exposure tests revealed that the composite coating had excellent corrosion resistance. Polarization curve tests showed that corrosion current density of the sample with composite coating was significantly decreased, indicating an effective corrosion protection of the composite coating. OCP measurement of the sample in NaCl solution demonstrated that the composite coating had the best cathodic protection effect. The good corrosion resistance of the composite coating was obtained by the synergy of SD and ZA coating.

SD/ZA coating can be used in marine environment to prolong the life of carbon steel fastener.

SD/ZA composite coating can reduce the risk and accident caused by failed fastener, avoid huge economic losses.

A new kind of composite coating was explored to protect the carbon steel fastener in marine environment. And the composite coating has the long-term anti-corrosion performance both in simulated and marine environment test.

Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.

First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.

The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.

A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

This research examines the effects of firm ownership and size on innovation capability using data from the World Bank China Enterprise Survey (WBCES), which provides directly measurable innovation-related variables. Key consideration is given to the role and innovation capability of state-owned enterprises (SOEs) compared with domestic and foreign private enterprises in the Chinese economy.

In its quest for technological self-reliance and a new developmental path, China is focusing on its enterprise innovation capability.

The findings suggest that SOEs and domestic private enterprises are similar in terms of innovation participation but differ in terms of innovation diversification, which implies ownership-specific innovative advantages. In general, the authors find that SOEs are more innovative with respect to processes innovation but less so with respect to product, management and promotion innovations. Foreign-owned enterprises are superior in all types of innovation except product innovation.

The authors also find that size is an important determinant of innovation capability, with the effect varying depending on location and industry. Moreover, the joint effect of firm ownership and size on innovation declines with increasing size. These findings provide new insights into the evaluation of China's major policies.

This research examines the effects of ownership and size on enterprise innovation capability, using the WBCES (2013) data, which include direct measurable innovation related variables.

The purpose of this paper is to set out a study of a Mannich base, which was synthesized and used as an acidizing corrosion inhibitor first, and to the corrosion inhibitor mechanism.

A Mannich base, 1-phenyl-3-(1-pyrrolidinyl)-propanone (PHPP), was synthesized with acetophenone, pyrrolidine and formaldehyde at pH = approximately 2-3. The structure of PHPP was characterized by elemental analysis and Fourier transform infrared spectroscopy (FTIR). The corrosion inhibition of PHPP on N80 steel in 15 per cent hydrochloric acid (HCl) was studied by weight loss method, scanning electron microscope (SEM) and energy dispersive X-ray analysis (EDAX), and the adsorption behavior of PHPP on the surface of N80 steel was discussed.

The results showed that the inhibition efficiency reached to 99.8 per cent and corrosion rate was 2.65 g·m-2·h-1 at 0.6 per cent of PHPP concentration in 15 per cent HCl, which indicated that PHPP presented excellent corrosion inhibition performance. The results of SEM and EDAX analysis showed that PHPP could be absorbed on the surface of N80 steel. The adsorption process of PHPP on the surface of N80 steel was chemisorption. This process was spontaneous and obeyed Langmuir adsorption isotherm.

It was found that PHPP presented excellent corrosion inhibition performance, and it is practicable to enhance oil production in oilfield development as a oil-well acidizing inhibitor. The study results can provide theoretical guidelines for the development of the inhibitor.

We investigate the interconnectedness between the financial sectors and new energy companies in China from the perspective of the multilayer network, and analyze the static and time-varying characteristics of the multilayer network at system and company levels, respectively.

We employ the multilayer network containing the realized volatility (RV here after) layer, the realized skewness (RS here after) layer and the realized kurtosis (RK here after) layer. The three realized indicators adopted to construct the multilayer network are generated by the intraday trading data from 2012 to 2022.

(1) Different layers have different characteristics, and can provide supplementary information. (2) Banks tend to play the role of risk transmitters on the whole, while the insurances and new energy companies tend to play the role of risk receivers on average. (3) The connectedness strength of financial sectors and new energy companies varies over time, and climbs sharply during the major crisis events. The roles of financial sectors and new energy companies may change from risk transmitters to risk receivers, and vice versa.

We adopt three realized indicators to construct the three-layer network, which provides a more comprehensive perspective for understanding the connectedness between the financial sectors and new energy companies in China.

Greenhouse gas (GHG) emission has a detrimental impact on climate change. There is an increasing trend for firms to use disclosure to signal stakeholders about its environmental responsibilities and performance in dealing with climate change. China is one of the countries producing the most carbon emissions. Over the last decade, Chinese state-owned enterprises (SOEs) are becoming important players in international trade. However, the existing literature provides limited evidence on how Chinese SOEs influence GHG disclosure. Through the lens of stakeholder–agency theory, this chapter studies the top 300 listed firms to examine the relationship between Chinese SOEs and the likelihood of GHG disclosure. The result suggests a negative relationship between Chinese SOEs and the likelihood of GHG disclosure. This could be explained as a consequence of the managers' political self-interests, economic and policy-oriented decision-making process and the power differentials between the government and SOE managers. This research extends the GHG literature to Chinese SOEs context, providing direct evidence on how state ownership impacts on GHG disclosure.

Recently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.

First, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.

Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.

To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.

This paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

This paper aims to exploit shape-memory polymers as self-healable materials. The underlying mechanism involved the thermal transitions as well as the enrichment of the healing reagents and the closure of the crack surfaces due to shape recovery. The multi-stimuli-triggered shape memory composite was capable of self-healing under not only direct thermal but also electrical stimulations.

The shape memory epoxy polymer composites comprising the AgNWs and poly (ε-caprolactone) were fabricated by dry transfer process. The morphologies of the composites were investigated by the optical microscope and scanning electron microscopy (SEM). The electrical conduction and the Joule heating effect were measured. Furthermore, the healing efficiency under the different stimuli was calculated, whose dependence on the compositions was also discussed.

The AgNWs network maintained most of the pathways for the electrons transportation after the dry transfer process, leading to a superior conduction and flexibility. Consequently, the composites could trigger the healing within several minutes, as applied with relatively low voltages. It was found that the composites having more the AgNWs content had better electrically triggered performance, while 50 per cent poly (ε-caprolactone) content endowed the materials with max healing efficiency under thermal or electrical stimuli.

The findings may greatly benefit the application of the intelligent polymers in the fields of the multifunctional flexible electronics.

Most studies have by far emphasized on the direct thermal triggered cases. Herein, a novel, flexible and conductive shape memory-based composite, which was capable of self-healing under the thermal or electrical stimulations, has been proposed.