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The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

Without competent and talented employees, no organisation can grow and sustain for a long time. It becomes essential for every organisation to retain and satisfy the employees to achieve their predetermined organisational goals. The present study examines the mediating effect of workplace spirituality dimensions (i.e. meaningful work, compassion, transcendence, mindfulness and sense of community) in the link between job satisfaction and organisational citizenship behaviour (OCB) among managerial employees of selected manufacturing firms of Chhattisgarh state.

Correlational research design was incorporated. Employees working at managerial positions at different private manufacturing firms of Chhattisgarh state were chosen as a sample for the present study. Regression analysis and confirmatory factor analysis tools were used to analyse the primary data collected from 400 respondents.

The results revealed that all the dimensions of workplace spirituality, i.e. meaningful work, compassion, transcendence, mindfulness and sense of community, were found statistically significant and partially mediated between job satisfaction and OCB among managerial employees of Chhattisgarh. The authors discussed the results thoroughly and provided avenues for the future research.

The findings of the present research study will assist all private organisations in rethinking their employee retention and satisfaction strategies, since the presence of workplace spirituality in the organisation has a significant and beneficial impact on its environment. The current research will assist organisations in creating circumstances for OCB for employee via the introduction of workplace spirituality.

Creating spirituality in the current situation, where Covid-19 has suddenly affected all organisations around the world, would be extremely beneficial in terms of employee retention and satisfaction, which would eventually aid in the development of an environment conducive to citizenship behaviour at the workplace. However, the role of workplace spirituality as a mediator in the link between job satisfaction and OCB is innovative and has received little attention in the research community.

The paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.

The paper used the vector error correction model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.

The conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.

This paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.

Maritime transportation plays an important role in facilitating both the global and regional merchandise trade, where accurate trend prediction is crucial in assisting decision-making in the industry. This paper aims to conduct a macro-level study to predict world vessel supply and demand.

The automatic autoregressive integrated moving average (ARIMA) is used for the univariate vessel supply and demand time-series forecasting based on the data records from 1980 to 2021.

For the future projection of the demand side, the predicted outcomes for total vessel demand and world dry cargo vessel demand until 2030 indicate upward trends. For the supply side, the predominant upward trends for world total vessel supply, oil tanker vessel supply, container vessel supply and other types of vessel supply are captured. The world bulk carrier vessel supply prediction results indicate an initial upward trend, followed by a slight decline, while the forecasted world general cargo vessel supply values remain relatively stable. By comparing the predicted percentage change rates, there is a gradual convergence between demand and supply change rates in the near future. We also find that the impact of the COVID-19 pandemic on the time-series prediction results is not statistically significant.

The results can provide policy implications in strategic planning and operation to various stakeholders in the shipping industry for vessel building, scrapping and deployment.

Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use.

This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges.

Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes.

Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government.

Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.

Green innovation and sustainability are two contemporary initiatives that are gaining more and more attention from researchers, academics and industry professionals as they are considered important business strategies to improve environmental conditions and obtain better organizational performance. Besides, the growth of uncontrolled economic activities leads to an imbalance of economic, social and environmental values in different sectors. However, little is known about the mediating role that economic, social and environmental sustainability has in the relationship between green innovation and firm performance. Previous literature has focused on developed economies, but not on a developing economy such as that of Mexico. Therefore, this research aims to fill this existing gap by exploring the mediating effects of sustainability in the relationship between green innovation and firm performance.

A theoretical research model that theorizes, through ten hypotheses, the antecedents and consequences of the mediating effect of economic, social and environmental sustainability and the occurrence between green innovation and firm performance is proposed. The model is tested through PLS-SEM using data that were collected using a questionnaire survey that was distributed among companies in the automotive industry in Mexico. In total, a sample of 460 responses was obtained.

The results suggest that green innovation has significant positive effects on economic and environmental sustainability, as well as on firm performance, but not on social sustainability. The results also indicate that the relationship between green innovation and firm performance improves considerably with the mediation of economic, social and environmental sustainability.

Despite the present study focused on an industrial sector that is commonly at the forefront of technological development, it was limited to a specific region of Mexico. Thus, its results must be taken with caution as more extensive results including other regions and nations will be required to further validate the results obtained from the present study.

The findings of this study have important implications for both policymakers and managers of manufacturing firms in the automotive industry as they can be used as a basis to formulate better strategies and policies to enhance the capabilities of companies to develop innovations that could reduce environmental risks and other consequences of climate change.

The present study adds to the innovation and sustainability body of knowledge by analyzing and discussing the mediating role of sustainability in the relationship between green innovation and firm performance. It also generates new knowledge about the mediating effect that sustainability has on the relationship between green innovation and firm performance, particularly in the context of a developing economy such as that of Mexico.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Eco-innovation is emerging as one of the most important constructs that improve environmental sustainability of firms. However, it has been shown that companies alone cannot adequately develop eco-innovation activities, which is why they require the implementation of external collaboration activities with intermediaries, suppliers and stakeholders to achieve a higher level of eco-innovation activities and improve business performance of manufacturing firms. Therefore, this research fills this gap by exploring the importance of the relationship between collaboration and eco-innovation.

The research is conducted through an extensive literature review with a research model consisting of 5 measurement scales, 24 items and 4 hypotheses. A self-administered questionnaire was distributed to a sample of 460 firms in Mexico, analyzing the data set through confirmatory factor analysis and structural equation models.

The results obtained from this study suggest that collaboration has significant positive effects both on the eco-innovation of products, processes and management, as well as on the business performance of companies in the automotive industry.

The findings of this study have important implications both for the public administration (e.g. development of policies to support companies and financing programs) and for the managers of companies in the automotive industry (e.g. training program for employees and collaboration with other firms).

This paper fills a research gap by expanding the limited body of knowledge that relates collaboration eco-innovation and business performance, which is why this research aims to fill this existing gap in the literature and explore the relationship between collaboration, eco-innovation and business performance.

Given the regional diversity in China, this study aims to provide an empirical evaluation of how organizational stakeholders (i.e. customers, employees, suppliers and shareholders) affect corporate environmental sustainability investment (ESI).

To empirically investigate the influence of organizational stakeholders on ESI, this study used regional-level data consists of Chinese A-share stocks for the years 2009–2019.

This study’s findings show that pressure from customers, employees and suppliers has a significant effect on corporate ESI, with customers being the most important stakeholder group. Shareholders, by contrast, have no significant influence on ESI. The influence of these pressures is more pronounced in developed regions (the east) than in less developed (the west) localities of China.

This study complements the stakeholder–institutional perspective by implying to consider the differentiated logics of the contesting stakeholders in the nonmarket operations.

Practically, this study poses that managers must realize the heterogeneity of pressures from stakeholders and the differentiated impact of these pressures keeping in view the institutional differences in different regions.

Our study reports initial empirical evidence that shows how regional differences influence the role of stakeholders in determining corporate environmental strategy.