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High-ductility cementitious composites (HDCC) have an excellent crack controlled capacity and corrosion resistance capacity, which has a promising application in structure engineering under harsh environment. The purpose of this study is to explore the corrosion mechanism of steel bar in HDCC.

Intact and the pre-cracked HDCC specimens under the coupled action of different dry–wet cycles and chloride attack were designed, and intact normal concrete (NC) was also considered for comparison. Corrosion behavior of a steel bar embedded in HDCC was analyzed by an electrochemical method, a chloride permeability test and X-ray computed tomography.

Steel corrosion probability is related to the chloride permeability of the HDCC cover, and the chloride permeability resistance of HDCC is better than that of NC. Besides, crack is the key factor affecting the corrosion of steel bars, and the HDCC with narrower cracks have a lower corrosion rate. Slight pitting occurs at the crack tips. In addition, the self-healing products and corrosion products fill up the cracks in HDCC, preventing the external aggressive ions from entering and thereby decreasing the steel corrosion rate.

HDCC has a superior corrosion resistance than that of NC, effects of variable crack width on corrosion behavior of steel bar in HDCC under the coupled actions of different dry–wet cycles and chloride attack are investigated, which can provide the guide for the design application of HDCC material in structure engineering exposed to marine environment.

The optimal control on reassembly (remanufacturing assembly) error is one of the key technologies to guarantee the assembly precision of remanufactured product. However, because of the uncertainty existing in remanufactured parts, it is difficult to control assembly error during reassembly process. Based on the state space model, this paper aims to propose the optimal control method on reassembly precision to solve this problem.

Initially, to ensure the assembly precision of a remanufactured car engine, this paper puts forward an optimal control method on assembly precision for a remanufactured car engine based on the state space model. This method takes assembly workstation operation and remanufactured part attribute as the input vector reassembly status as the state vector and assembly precision as the output vector. Then, the compensation function of reassembly workstation operation input vector is calculated to direct the optimization of the reassembly process. Finally, a case study of a certain remanufactured car engine crankshaft is constructed to verify the feasibility and effectiveness of the method proposed.

The optimal control method on reassembly precision is an effective technology in improving the quality of the remanufactured crankshaft. The average qualified rate of the remanufactured crankshaft increased from 83.05 to 90.97 per cent as shown in the case study.

The optimal control method on the reassembly precision based on the state space model is available to control the assembly precision, thus enhancing the core competitiveness of the remanufacturing enterprises.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

Technology innovation in enterprises is a powerful driver of national competitiveness and sustainable corporate development. At the same time, the regional innovation policy mix serves as a core factor at the macro level, guiding and influencing enterprise technology innovation. Therefore, this paper addresses a critical question in innovation studies: the impact of the regional innovation policy mix complexity on enterprise technology innovation. Additionally, we also investigated the internal mechanisms and boundary conditions within this framework.

A dual-mode network model of local government-regional innovation policy is developed to capture the complexity of the regional innovation policy mix. The complexity index is calculated iteratively using the R language. The paper employs quantitative and empirical analysis, drawing on a sample of 622 regional innovation-related policy documents from 31 Chinese provinces (municipalities and autonomous regions).

The results reveal an inverted U-shaped relationship between policy mix complexity and enterprise technological innovation. The analysis further shows that university-industry cooperation intensity mediates this relationship, while regional knowledge absorptive capability moderates the impact of regional innovation policy mix complexity on enterprise technological innovation.

This paper highlights the influence of regional innovation policy mix complexity on enterprise technological innovation and underscores the role of university-industry cooperation intensity and regional knowledge absorptive capability. The findings offer valuable insights into the dynamics of enterprise innovation and inform effective government policy governance for fostering innovation.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.

Megaprojects exert a significant impact on sustainable development, and it is imperative for stakeholders to collectively ensure their development occurs in a socially responsible manner. While there has been a growing focus on the involvement of megaprojects in social responsibility, scant attention has been given to understanding the collective actions of stakeholders in implementing social responsibility within these projects. Specifically, the institutional mechanism leading megaproject stakeholders to engage in socially responsible collective action is largely unexplored. To fill this gap, this study primarily aims to explore the institutional antecedents influencing socially responsible collective action in megaprojects.

Drawing on institutional theory, this study empirically examines the factors influencing socially responsible collective action in megaprojects. An online questionnaire survey was administered to collect data from 365 participants engaged in mega water transfer projects in China. The data analysis employed the partial least squares structural equation modeling technique.

The findings from the partial least squares analyses indicate that coercive isomorphism, mimetic isomorphism, and normative isomorphism all demonstrate positive associations with stakeholders’ intention to engage in socially responsible collective action. Moreover, the findings also show a positive correlation between stakeholders’ intention and their behavior in participating in socially responsible collective action within megaprojects. Additionally, coercive isomorphism positively moderates the connection between mimetic isomorphism and the intention to engage in SRCA, while negatively moderates the relationship between normative isomorphism and the intention to undertake socially responsible collective action.

This study enriches the existing body of knowledge by identifying coercive, mimetic, and normative isomorphism as antecedents to adopting socially responsible collective action in megaprojects. Furthermore, the study enhances our comprehension by demonstrating that stakeholders’ intention to fulfill social responsibility translates into tangible actions. The implications and recommendations provided shed light on how various types of institutional isomorphism can be used to encourage stakeholders to embrace socially responsible collective action in megaproject management.