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The restoration and strengthening of QT600 is an industry bottleneck challenge. The Co-12 cladding layer has great wear and corrosion resistance. The purpose of this paper is to quantitatively reveal the transient evolution law of the corrosion process of Co-12 cladding layer on QT600 surface.

In this paper, a three-dimensional numerical model of the corrosion process of Co-12 cladding layer by QT600 laser cladding is established. The interaction between pitting pits and corrosion medium is considered to reveal the transient evolution of ion concentration, electrode potential, pH and corrosion rate at different locations.

The calculation shows that the ion concentration in pitting pit changes Cl⁻>Co²⁺>Na⁺, pH value decreases from top to bottom and corrosion rate at bottom is greater than that at top. The electrochemical corrosion test of Co-12 cladding layer was carried out. It is shown that the current density of QT600 increases by an order of magnitude compared to the Co-12 cladding layer, and the corrosion rate is 4.862 times higher than that of the cladding layer.

The results show that Co-12 cladding layer has great corrosion resistance, which provides an effective way for QT600 protection.

This study investigates the impact of ESG rating disagreements on stock performance in the Chinese A-share market, focusing on immediate and short-term market reactions and the risk of future stock price crashes. Using data from the Shanghai and Shenzhen stock exchanges, we analyze 17,006 firm-year observations from 2010 to 2021. Stock return data are sourced from the Wind database, while additional financial metrics are obtained from the China Stock Market and Accounting Research (CSMAR) database. Corporate governance information is drawn from the China National Research Data Service (CNRDS) database. Our findings indicate that higher levels of ESG divergence significantly increase the risk of future stock price crashes. Furthermore, the presence of independent directors moderates this relationship, reducing the likelihood of such crashes. Immediate market reactions to ESG rating disagreements are also significant, underscoring the need for transparency and alignment among rating agencies. The study highlights the importance of robust corporate governance and standardized ESG rating methodologies to mitigate associated risks. Policy recommendations include promoting transparency in ESG rating processes and enhancing the role of independent directors in corporate governance.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

This study empirically tests the influence of key elements on intent to use electronic (E)-ticket through the “Stimulus–Organism–Response (SOR)” framework and structural equation model. Results highlight factors such as E-trust Technology, Ease of Use, E-satisfaction, Intention to Purchase E-ticket, Price Perception, and Usefulness; the study comprehensively analyzes the factors influencing the decision-making process of consumers when it comes to purchasing E-tickets. The research employs a hypothesis-driven approach and gathers survey results from 408 observants to find out the intention of consumers to use E-tickets for using transportation services, which help transportation providers understand the importance of its platform to benefit customers who are willing to change their perceptions from paper tickets to E-tickets, the reason customers buying E-ticket rather than paper ticket while using digitalization to help firms control their cost and building internal legitimacy by better managing their internal stakeholder.

The study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.

Utilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.

Analysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.

This research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Instrumental and emotional information influence paradoxically on people’s decision-making, and similar influences are more evident in e-commerce scenarios where physical information is limited. This study aims to construct a systematic explanatory framework for the influence of multidimensional recommendation information diversity (RID) on users' click and purchase decisions based on the social support theory (SST).

This study analyses 453,176 data from 67,079 users of a Chinese e-commerce platform, applying lasso algorithmic techniques and cross-fit partialling-out (XPO) regression for empirical analysis.

The study finds that instrumental support information diversity (ISID) and emotional support information diversity (ESID) play divergent roles, and that the effects of both on user decision-making are inconsistent with mode-flip and marginal change. Differences in users' information craving and information overload processing mechanisms for instrumental and emotional information, leading to an inverted U-shaped effect of ISID on consumption decisions, while ESID has a U-shaped effect. Additionally, supplier certification eliminates the marginal change in ESID, and products with a high information standardisation degree eliminate the marginal change in ISID.

Research results reveal the opposing roles of the two types of RID and the application boundaries of their roles, providing empirical evidence for academic research.

This study aims to explore the spatio-temporal dynamic characteristics and influencing factors of the coordination degree of the three systems of digital economy, energy and human habitat in Western China and to provide academic research support for promoting coordinated and sustainable development in similar regions of the world.

Based on system theory and sustainable development theory, this study primarily uses the coupled coordination degree model to assess the degree of coordination between the three systems.

The findings of this study indicate that: The three systems’ overall coordination is low. The distribution of the degree of coordination has spatial differences and its coefficient of variation is small. The probability of the coordination type changing for the better is greater than that of the opposite, and neighboring provinces interact with one another. The old-age dependence ratio, the resident population’s urbanization rate and public budget expenditure have the strongest gray association with the degree of coordination.

This study’s findings will be valuable for policymakers in developing policies to promote the coordinated and sustainable growth of the region’s digital economy, energy and human habitat. Additionally, the findings will aid in facilitating regional exchanges and cooperation to enhance the level of sustainable development.

This study’s findings will contribute to increased social interest in coordinating sustainable growth in the digital economy, energy and human habitat.

This study examines the digital economy, energy and human habitat within the same framework and investigates spatial spillover effects using spatial Markov chains.

Although prior research has documented substantive knowledge of the benefits of high-performance work systems (HPWS), results regarding both sides of HPWS are inconsistent. To reconcile these inconsistencies, the authors identified two specific HR attributions—employee well-being HR attribution and performance HR attribution, and examined their roles in the relationship between team-level HPWS and employees' thriving at work and emotional exhaustion.

The authors collected multi-source data from 36 team leaders and 181 individuals. Given the nested nature of the data, the authors used Mplus 7.4 to conduct multilevel structural equation modeling for hypothesis testing.

The results showed that team-level HPWS and employee well-being HR attribution interact to affect psychological availability, which subsequently promotes thriving at work. However, team-level HPWS and employee performance HR attribution do not interact to influence role overload/psychological availability; team-level HPWS and employee well-being HR attribution do not interact to affect role overload.

Current literature has overlooked identifying key contingencies for both sides of HPWS effects on employee outcomes. Therefore, this study developed a mediated moderation model and incorporated HR attributions to explore two distinct pathways by which HPWS affects employees' thriving at work and emotional exhaustion. The present study helps to reconcile the inconsistent findings regarding the HPWS double-edged sword nature. In addition, the authors focused on HPWS at the team level, which is also underexplored in the existing HPWS research.

This study investigates the impact of residential type and neighborhood security on public trust in the police in Ghana, while controlling for demographic factors and police-related variables.

Data were collected during the ninth round of the Afro-Barometer survey conducted in Ghana between 2019 and 2021 with a sample size of 2,369 participants. The study employed binary logistic regression analysis to examine the relationship between the independent variables (residential type and neighborhood security) and the dependent variable (trust in the police).

The results indicate that living in traditional housing is associated with lower levels of trust in the police compared with other residential types. Unexpectedly, neighborhood security did not emerge as a statistically significant predictor of police trust. However, police corruption and the use of force were negatively associated with trust, whereas police professionalism positively predicted trust. Interestingly, unnecessary police stops were positively associated with trust, possibly reflecting a complex relationship between police visibility and public perceptions. This study also revealed ethnic and regional variations in police trust, highlighting the need for culturally sensitive policing approaches.

This study stands out in three key aspects. First, it represents one of the first attempts to examine how residential type and neighborhood security influence public trust in law enforcement agencies in Ghana. Second, this study is among the few to investigate the relationship between neighborhood conditions and trust in police using a sample that is representative of the entire nation. Finally, these findings contribute to the understanding of the multifaceted nature of public trust in the police within the Ghanaian context and offer insights for policymakers and law enforcement agencies to enhance police-community relations.

Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.