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This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

This study aims to analyze and discuss the impact of corporate social responsibility (CSR) on firms’ performance, as well as to examine the interplay between CSR and the economy, society and innovation.

This paper collects data from 420 manufacturing firms across various geographical regions in China. By using a structural equation model, the paper investigates the impact of CSR on enterprise innovation, customer management capability, market competitiveness (MC) and firm financial performance.

The findings demonstrate that CSR performance positively contributes to enhancing the level of enterprise innovation, as well as customer management capability and market competitiveness. Furthermore, it assists enterprises in improving market competitiveness and elevating customer management capabilities. Thus, CSR can have a positive effect on the firm financial performance.

The outcomes presented in this paper offer valuable evidence regarding the influence of implementing CSR on different aspects of enterprise performance and innovation. Moreover, it provides practical recommendations for enterprises seeking to transition towards low-carbon practices and upgrade their manufacturing industry.

In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC).

This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability.

The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises.

For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively.

This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

To remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.

Based on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.

According to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.

The paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

The chapters emphasise the importance of data management from the perspective of the business management process, where big data is the most crucial and pressing technical and business issue in the modern realm of technology. The same data has a significant influence on the current financial environment. Organisations are facing challenges in explicating complicated financial data manually and using it to drive their decision-making processes. Data-driven decision-making is a dominant tool for any professional. It enhances precision, alleviates risk, improves efficacy, aids financial management, offers customer insights, provides a competitive edge, supports strategic planning, enables performance tracking, fosters innovation and has predictive capabilities. The power of data makes the organisation more prosperous and resilient in the face of change. By making informed decisions based on data and analytics, organisations can unlock their full potential and achieve sustainable growth. The chapter suggests a data-driven culture in the organisation with the help of strategising in terms of data collection, analytics and data management by establishing governance and regulatory practices to ensure data security and integrity. The latter part covers the forecasting and transformative ability of data by integrating machine learning and deep learning models. The chapter also covers the visualisation perspective of the data by transforming the information into a visual setting, illuminating the hidden insights and making them tangible and relatable. The chapter closes with a suggestion for managers to stay competitive, make more reasoned and sound decisions and adapt to the evolving business environment.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

The purpose of this paper is to unveil the efficacy of coaching leadership within Chinese organizations and bolster employees’ work engagement.

The sample data were collected through employing the questionnaire method. The participants consisted of 234 employees and 53 supervisors in Chinese enterprises. Hypothesis testing was conducted using multiple regression analysis and the Bootstrap method.

The coaching leadership exhibited a positive association with employees’ work engagement, psychological safety and self-efficacy. It was observed that employees’ psychological safety and self-efficacy played a dual-mediation role between coaching leadership and work engagement. Additionally, employees with power distance orientation (POD) amplified the positive effects of coaching leadership on psychological safety and self-efficacy.

This study contributes to the literature on coaching leadership and work engagement by elucidating their direct influence, as well as the dual-mediating roles of psychological safety and self-efficacy. Besides, our findings underscore the moderating effect of POD in amplifying the impacts of coaching leadership. However, the nonlongitudinal survey design adopted by our study should be noted for its potential limitations in establishing causality.

The findings demonstrate that coaching leadership, psychological safety and self-efficacy play a crucial role in fostering work engagement. Employees with higher POD are more likely to benefit from coaching leaders.

This study contributes to coaching leadership literature and provides insights into how and when coaching leadership affects work engagement in Chinese organizations.