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Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

The purpose of this paper is to study the variations of composition and properties of the passive film on 316 L stainless steel surface in 80°C, 0.5 mol L-1 H2SO4 + 2 mg L-1 NaF solution, is helpful to understand the mechanisms of corrosion resistancethe of plated Pd on 316 L ss.

The variations of composition and properties of the passive film on 316 L stainless steel surface in 80°C, 0.5 mol L-1 H2SO4 + 2 mg L-1 NaF solution after connected to Pd electrode were studied with methods of potential monitor, X-ray photoelectron spectroscopy analysis and electrochemical impedance spectrum (EIS) measurement.

By connecting to a Pd electrode, the potential of the SS sample increased from the active region to the passive region. By connecting to the Pd electrode, the contents of Cr, Cr(OH)3 and Fe3O4 in passive film increased obviously. With increased Pd/SS area ratio, the Cr(OH)3 content in passive film increased but the Fe3O4 content changed little. The results show that after connecting to Pd the corrosion resistance of the passive film on 316 L stainless steel increases obviously, which may be attributed to the more compact passive film because of higher Cr, Cr(OH)3 and Fe3O4 contents and less point defects in the film.

The effects and mechanism of Pd on passivation of SS was studied.

As Chinese imported cross-border e-commerce has entered a stage of rapid development, the problem of consumer shopping risk is increasingly prominent and the crisis of consumer trust is intensified. The theory of establishing consumer trust in traditional online shopping can no longer meet the need of cross-border context.

The researchers used the methods of network logs and grounded theory. The data collection and analysis are conducted on consumer comments from Tmall Global, NetEase Koala and JD Worldwide in the product comment area. This article explored and extracted the moderating variables of consumer perceived risk and cross-border characteristics in cross-border e-commerce. Based on the theory of “perceived risk – consumer trust – consumer purchase decision – making,” this article deduced mechanism of consumer dynamic trust based on the whole process of cross-border e-commerce transaction.

In the prepurchase, purchase and postpurchase stages of cross-border e-commerce transactions, consumers' perceived cognitive risk, transaction risk and utility risk are moderated by the cultural distance, geographical distance and institutional distance caused by the cross-border transaction subjects. On this basis, the preinfluence factors of trust in each transaction stage are synthesized to respectively influence the establishment of cognitive trust, emotional trust and behavioral trust, so as to affect consumers to make the order payment, confirm receipt and praise repurchase decisions. At the same time, with the advance of prepurchase, purchase and postpurchase transactions in cross-border online shopping, consumer trust presents a dynamic evolutionary path of “cognitive trust – emotional trust – behavioral trust.”

This article expands the application context of the theory of consumer rational behavior from traditional online shopping to the context of cross-border online shopping and expands the scope of interpretation of the theory of consumer rational behavior. This article also supplements the theoretical gaps in the dynamic evolution of consumer trust in cross-border online shopping, enriches the decision-making process model of consumers in the context of cross-border online shopping and provides new ideas for follow-up research.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

This study aims to explore the critical influencing factors that lead to the site selection failure of waste-to-energy (WtE) projects in China under the influence of the “Not In My Back Yard” (NIMBY) effect, which can provide references to improve the decision-making process of similar projects in the future.

The fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was used to propose an analytical framework for exploring the critical influencing factors affecting the site selection failure of WtE projects. The causal relationship between different influencing factors is finally determined on the basis of the opinions of 12 experts from universities, government departments, consulting units, planning and design units, construction units and WtE enterprises.

Results showed that six crucial factors resulted in the site selection failure of WtE projects from the NIMBY effect perspective: “Insufficient public participation,” “Near the place of residence,” “Nonstandard government decision-making processes,” “Low information disclosure,” “Destroys the surrounding environment,” and “Imperfect compensation scheme.”

Results can determine the priorities and causal relationships among the various influencing factors. The decision-making optimization suggestions can provide reference for decision- makers, thereby possibly promoting the scientific and standardization of site selection decision process.

Drawing on a costs-benefit analysis perspective, the purpose of this paper is to investigate the relationship between managerial openness and employee voice and its boundary conditions.

This study collected three waves of data by surveying 326 pairs of employees and their supervisors. The hypotheses were tested by using Hayes’s (2018) SPSS macro application with a bootstrap approach to obtain confidence intervals.

Managerial openness facilitates employee voice by decreasing perceived voice costs. Felt obligation positively moderates the direct as well as the indirect relationship between perceived voice costs and employee voice.

This study uncovers the alternative mechanism underlying the relationship between managerial openness and employee voice as well as the boundary condition of this relationship.

Based on conservation of resources (COR) theory, this study aims to examine the impact of daily illegitimate tasks on employees' daily silence and daily voice behavior, as well as the mediating role of daily ego depletion and the moderating role of trait mindfulness.

Through daily diary approach, 81 employees were followed for 10 consecutive workdays. Multilevel analysis was employed to examine the hypothesized relationships.

The results showed that daily illegitimate tasks are positively related to daily silence behavior and negatively related to daily voice behavior; daily ego depletion plays a mediating role in these relationships. Trait mindfulness moderates the effect of daily illegitimate tasks on daily ego depletion and the indirect effect of daily illegitimate tasks on daily silence and daily voice.

Managers should be mindful of minimizing the assignment of illegitimate tasks. Additionally, it is recommended that the organization provide training courses for employees to help them reduce ego depletion. Finally, organizations should focus on fostering high levels of mindfulness among their employees.

This study contributes to the existing literature by investigating the immediate impact of illegitimate tasks on employee voice and silence at within-person level. By doing so, it enhances comprehension of the consequences associated with illegitimate tasks. Meanwhile, this study offers additional insights into the underlying mechanisms and boundary conditions of the effect of illegitimate tasks from a resource perspective.

With the rising concerns of compulsive use of social media, it is important to understand why users develop such unplanned and irrational behaviors. Leveraging the uses and gratification theory, the authors aim to explore the determinants of compulsive use of social media from the dual perspectives of individual needs (need to belong (NTB) and need for uniqueness) and peer-related factors (referent network size and perceived peer activeness). Due to the importance of self-construal in cognitive deliberation on peer influences, the moderating effects of self-construal were taken into consideration.

The authors empirically test their model by conducting an online survey with 459 WeChat users.

The results show that compulsive use of social media is predicated by both individual needs and influence from peers. Moreover, peer influence could be attenuated when individuals develop a high degree of independent self-construal.

The authors' study contributes to the research of compulsive behavior in the context of social media use by incorporating the dual effects of individual needs and social influence. The authors also offer managerial insights on eradicating the formation of compulsive behaviors.

The authors examine the dual effects of individual needs and peer influence in predicting compulsive use of social media and the moderating role of self-construal, which have been rarely investigated in this context.

This study proposes that the three core underpinnings of dynamic managerial capabilities (DMCs) – managerial cognition, managerial human capital and managerial social capital – represent individual-level micro-foundations that influence corporate social responsibility (CSR). It further explores the interaction mechanism between the three underpinnings in influencing CSR, and their influence depends on the technological turbulence caused by big-data-related technologies.

This study uses a quantitative research method and partial least squares structural equation modelling (PLS-SEM) to test the relationship between latent factors based on a sample of 270 Chinese top managers.

The three core underpinnings of DMCs are positively related to CSR. Managerial human capital and managerial social capital mediate the relationship between managerial cognition and CSR. Technological turbulence's moderating effects are also tested. Specifically, technological turbulence amplifies the positive relationship between managerial cognition, managerial human capital and CSR but negatively moderates the relationship between managerial social capital and CSR.

Why are some firms more willing to participate in CSR than others mainly depend on the fact that the actual participants of CSR are the top managers who formulate strategies and implement CSR plans. This study, grounded in the DMCs framework and the upper echelons perspective, is arguably the first to link DMCs' three core underpinnings and CSR, and further explore the multiple drivers' mechanisms and boundary conditions. This study contributes to individual micro-foundation of CSR literature, and advances the understanding of whether and how top managers influence CSR engagement.

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.