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The purpose of this paper is to focus on the galvanic corrosion behaviors of the low-carbon ferritic stainless steel electrical resistance welding (ERW) joint in the simulated seawater.

The electrochemical methods such as electrochemical noise, galvanic current and TOEFL polarization curve tests were used to study the galvanic corrosion behaviors of ERW joints of low-carbon ferritic stainless steel in simulated seawater. On this basis, a reliable accelerated corrosion method was developed.

The corrosion type of the base metal and joint is the typical local corrosion. The order of corrosion resistance from strong to weak is: weld zone > base metal > low-temperature heat-affected zone (HAZ) > high-temperature HAZ. The results of constant current-constant potential accelerated corrosion test show that after constant current-constant potential accelerated corrosion, the joints present a typical groove corrosion pattern. The groove initiating area is located in the HAZ, and the corrosion degree in the weld zone is relatively light, which is consistent with the electrochemical test results.

This paper has clarified the galvanic corrosion behaviors of low-carbon ferritic stainless steel ERW joints. Moreover, a reliable accelerated corrosion method for the low-carbon ferritic stainless steel ERW joint has been developed.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.

The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.

This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.

An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

Ethical consumption is an integral component for the sustainable development in the world and is especially challenging in the Western consumer society. This research demonstrates that mindfulness, a Buddhism-based notion, is associated with two related and distinctive approaches of ethical consumption: refinement and reduction. It examines the psychological mechanisms underlying the effects of mindfulness on these two approaches of ethical consumption.

Self-report data were collected through an online survey with consumers from western societies (N = 523).

The findings show (1) that the significance of mindfulness on both approaches of ethical consumption and (2) that the contrast between the different mechanisms underlying them. Specifically, the mindfulness–consumption refinement link is fully mediated by connectedness-to-nature whereas the mindfulness–consumption reduction link is fully mediated by connectedness-to-nature and self-control. A series of supplementary studies further confirmed the proposed model.

It demonstrates the multifaceted and complex nature of ethical consumption, which is positively associated with mindfulness but through distinctive psychological mechanisms.

The multifaceted and complex nature of ethical consumption and its underlying drivers need special attention. Mindfulness can be an effective means to boost ethical consumption behavior. Meanwhile, nurturing the sense of connectedness to nature and self-control capability facilitates the path-through of the positive impacts of mindfulness

The findings can be adopted to enhance the effectiveness of mindfulness practice in promoting ethical consumption towards achieving the Sustainable Consumption goal, especially in the West.

The paper makes original contribution by conceptualizing two interrelated and distinctive approaches of ethical consumption and shows how mindfulness promotes both through different mediating pathways. Overall, this study paints a clearer picture how mindfulness relates to ethical consumption.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

This paper aims to study the effects of the different processing parameters in the reciprocating magnetorheological polishing (RMRP) on the surface roughness (Ra) and material removal rate (MRR) of the workpiece surface.

The principle of RMRP method is discussed, and a series of the single factor experiments are performed to evaluate the effects of the workpiece’s rotational speed, the reciprocating frequency, the magnetic field strength, the working gap and the processing time on machining results using the RMRP device.

The RMRP method is effective and practical for K9 glass polishing, and the optimized processing parameters are obtained by the single factor experiments of RMRP. The surface roughness of the workpiece is reduced from 332 nm to 28 nm under optimized processing parameters.

In the present study, the RMRP method is proposed, and a system of experiments is carried out using the RMRP device. The RMRP device can improve the surface roughness and MRR of the K9 glass significantly. Furthermore, the test results provide references for reasonable selection of the processing parameters in magnetorheological polishing process.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

This research aims to explore the interactions of stakeholders in online food delivery (OFD) platforms, including restaurants, the delivery system and the platform, and the effect on total sales. In order to comprehensively analyze the service of stakeholders, the authors adopted the time duration which provides a unified metric for assessing service performance.

The authors used panel vector autoregressive (PVAR) model analysis to verify the interactions among restaurants, the delivery system, and the platform itself, and to assess their effects on the sales. Data were collected from one of the biggest OFD service platforms in China – Eleme.com.

First, the findings confirmed that the service performance of restaurants, the delivery system and the platform itself influence one another. There is a bidirectional causality between food preparation time (FPT) and actual delivery time (ACDT), as well as between FPT and advance arrival time (ADAT), ACDT and ADAT. Second, the service performance of restaurants, the delivery system, and the platform itself all positively affect sales, thus demonstrating unidirectional causality.

The current research is a pioneering empirical study, as it confirms the dynamic interactions between participants in OFD platforms and the dynamic influence of their respective service performances on sales. The findings of this research have a number of implications for the management and operation of online food ordering and delivery platforms.

The primary purpose of this study is to investigate the influence of tourists' perceived value, satisfaction and behavioral intention on the development of Lipu Yi costume culture to promote the development of intangible cultural tourism and better construct a model of the influencing factors of Lipu Yi costumes in the development of intangible cultural heritage tourism.

The study site is the intangible cultural district of Panzhihua, Sichuan Province, China. This study examines the interrelationships between tourists' perceived value of experience, behavioral intention and satisfaction as the tourists relate to Lipu Yi costume and intangible cultural heritage tourism. A sample of 225 tourists who had visited Panzhihua at least once was selected for the study.

All seven of the survey's hypotheses were supported. Therefore, this study concludes that tourists' perceived value, satisfaction and behavioral intention directly affect the development of intangible cultural tourism and significantly positively impact the growth of Lipu Yi costumes culture. Descriptive analysis, confirmatory factor analysis (CFA) and structural equation modeling (SEM) investigation methods were used.

This paper analyzes tourists' perceived value of Lipu costume culture and tourists' satisfaction and behavioral intention during the tourism process. This study provides a more in-depth understanding of the relationship between Lipu Yi costume and non-heritage tourism factors. Practical methods and approaches are sought to further develop Lipu Yi costume non-heritage tourism.