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To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This paper aims to present a control method to realize the constant force grinding of automobile wheel hub.

A constant force control strategy combined by extended state observer (ESO) and backstepping control is proposed. ESO is used to estimate the total disturbance to improve the anti-interference and stability of the system and Backstepping control is used to improve the response speed of the system.

The simulation and grinding experimental results show that, compared with the proportional integral differential control and active disturbance rejection control, the designed controller can improve the dynamic response performance and anti-interference ability of the system and can quickly track the expected force and improve the grinding quality of the hub surface.

The main contribution of this paper lies in the proposed of a new constant force control strategy, which significantly improved the stability and precision of grinding force.

Considering the influence of environmental noise and modeling error during the process of the robotic automatic grinding aero-engine blade, this study aims to propose a method based on the extended state observer (ESO) to reduce the fluctuation of normal grinding force.

First, the measurement range of the six-dimensional force sensor is calibrated according to the maximum acceleration of end-effector and grinding force. Second, the gravity and zero drift compensation model is built to compensate for measurement error. Finally, the switching function is designed based on the difference between the expected grinding force and the actual feedback value. When the value of function stays within the switching band, a nonlinear active disturbance rejection control (ADRC) loop is applied. When the function value reaches outside the switching band, an ESO-based sliding mode control (SMC) loop is applied.

The simulated and experimental results show that the proposed control method has higher robustness compared with proportion-integral-derivative (PID), Fuzzy PID and ADRC.

The processing parameters of this paper are obtained based on the single-factor experiment without considering the correlation between these variables. A new control strategy is proposed, which is not only used to control the grinding force of blades but also promotes the development of industrial control.

ESO is used to observe environmental interference and modeling errors of the system for real-time compensation. The segment control method consisting of ESO-based SMC and ESO-based ADRC is designed to improve the robustness. The common application of the two parts realizes suppression of fluctuation of grinding force.

This study aims to realize the constant force grinding of automobile wheel hub.

A force control strategy of backstepping + proportion integration differentiation (PID) is proposed. The grinding end effector is installed on the flange of the robot. The robot controls the position and posture of the grinding end actuator and the grinding end actuator controls the grinding force output. First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. Finally, the feasibility of the proposed method is verified by simulation and experiment.

The simulation and experimental results show that the backstepping + PID strategy can track the expected force quickly, and improve the dynamic response performance of the system and the quality of grinding and polishing of automobile wheel hub.

The mathematical model is based on the pneumatic system and ideal gas, and ignores the influence of friction in the working process of the cylinder, so the mathematical model proposed in this study has certain limitations. A new control strategy is proposed, which is not only used to control the grinding force of automobile wheels, but also promotes the development of industrial control.

The automatic constant force grinding of automobile wheel hub is realized, and the manpower is liberated.

First, the modeling and analysis of the grinding end effector are carried out, and then the backstepping + PID method is adopted to control the grinding end effector to track the expected grinding force. The nonlinear model of the system is controlled by backstepping method, and in the process, the linear system composed of errors is obtained, and then the linear system is controlled by PID to realize the combination of backstepping and PID control.

The purpose of this paper is to present a crack initiation mechanism of the external hydrogen effect on type 304 stainless steel, as well as on fatigue crack propagation in the presence of hydrogen gas.

The effects of external hydrogen on hydrogen-assisted crack initiation in type 304 stainless steel were discussed by performing fatigue crack growth rate and fatigue life tests in 5 MPa argon and hydrogen.

Hydrogen can reduce the incubation period of fatigue crack initiation of smooth fatigue specimens and greatly promote the fatigue crack growth rate during the subsequent fatigue cycle. During the fatigue cycle, hydrogen invades into matrix through the intrusion and extrusion and segregates at the boundaries of α′ martensite and austenite. As the fatigue cycle increased, hydrogen-induced cracks would initiate along the slip bands. The crack initiation progress would greatly accelerate in the presence of hydrogen.

To the best of the authors’ knowledge, this paper is an original work carried out by the authors on the hydrogen environment embrittlement of type 304 stainless steel. The effects of external hydrogen and argon were compared to provide understanding on the hydrogen-assisted crack initiation behaviors during cycle loading.

The serious wear problem of heavy-haul freight vehicle wheels affects the safety and economy of vehicle operation. This paper aims to study wheel wear evolution law and the influence of line parameters on wheel wear of heavy-haul freight, and provide the basis for operation and line maintenance.

The wheel wear test data of heavy-haul freight vehicles were analyzed. Then a heavy-haul freight vehicle dynamic model was established. The line parameters influencing wheel wear in heavy-haul freight vehicles were also analyzed by the Jendel wear model, and the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear were analyzed.

A rail cant of 1:40 results in less wheel wear; an increase in the rail gauge can reduce wheel wear; and when matched with the CHN60 rail, the wear depth is relatively small. A decrease of 9.21% in wheel wear depth when matched with the CHN60 rail profile. The ramp of the heavy-haul line is necessary to consider for calculating wheel wear. When the ramp is considered, the wear depth increases by 8.47%. The larger the ramp, the greater the braking force and therefore, the greater of the wheel wear.

This paper first summarizes the wear characteristics of wheels in heavy-haul freight vehicles and then systematically analyzes the effect of line parameters on wheel wear. In particular, this study researched the effects of rail cant, rail gauge, rail profile and line ramp on wheel wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0038/

The purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.

Different from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.

A case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.

The casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.

Considering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.

(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

The purpose of this paper is to propose a fractional order optimization method based on perturbation bound and gamma function of a DGM(r,1).

By analyzing and minimizing perturbation bound, the sub-optimal solution on fractional order interval is obtained through offline solving without iterative calculation. By this method, an optimized fractional order non-equidistant ROGM (OFONEROGM) is applied in fitting and prediction water quality parameters for a surface water pollution monitoring system.

This method can narrow fractional order interval in this work. In a surface water pollution monitoring system, the fitting and prediction performances of OFONEROGM are demonstrated comparing with integer order non-equidistant ROGM (IONEROGM).

A method of offline solving the sub-optimal solution on fractional order interval is proposed. It can narrow the optimized fractional order range of NEROGM without iterative calculation. A large number of calculations are eliminated. Besides that, optimized fractional order interval is only related to the number of original data, and convenient for practical application. In this work, an OFONEROGM is modeled for predicting water quality trend for preventing water pollution or stealing sewage discharge. It will provide guiding significance in water quality parameter fitting and predicting for water environment management.

Drawing from social capital theory and resource dependence theory, this paper aims to test the relationship between top management team (TMT) government social capital and firm’s innovation performance via firm’s network prestige, and the moderating effect of TMT academic social capital.

The authors collected data from the China Stock Market and Accounting Research Database as well as A-share listed firms’ annual reports, and finally generated a sample of 922 firms and 2,464 firm-years from 2008 to 2014. UCINET 6.0 was used to analyze the data.

The authors find that the government social capital of TMT is positively related to firms’ innovation performance and firms’ network prestige plays a mediating role in this relationship. In addition, TMT academic social capital can strengthen the links between TMT government social capital and innovation performance through firms’ network prestige.

This paper not only contributes to literatures on the mechanism in the relationship between government social capital and firms’ innovation, but also to literatures on the effectiveness of the heterogeneity of firm’s social capital.

Entrepreneurial process strongly relies on context. The previous entrepreneurship research in developed countries over-emphasizes on its economic impact, but ignores its social impact, which leads to the slow development of entrepreneurship theories. Transitioning China provides entrepreneurs with a typical environment where opportunities and constraints coexist, which is a new research area in the field of entrepreneurship study.

Based on the grounded theory approach, this paper generalizes a local entrepreneurial process model from a multiple case study.

The paper states that the interaction among entrepreneurs, opportunities and institutional context is the core of the process. To be specific, entrepreneurial process includes an inner and an outer mechanism. The inner mechanism is based on the relations among institutional constraints, entrepreneurs and opportunities: nascent entrepreneurs, forced by institutional constraints to start a business, undergo a psychological process with entrepreneurial angst, reflective learning and effectuation and finally create business opportunities. The outer mechanism is grounded in the relations among new ventures, institutional evolution and opportunity development: new ventures facilitate institutional evolution through institutional entrepreneurship strategies, which in turn supports the ventures in the sustainable development of opportunities.

This study illuminates the social and institutional impact of entrepreneurial behavior, which is gradually fading and forgotten in modern society. The findings of the study enrich the research on entrepreneurial process, entrepreneurial cognition and institutional entrepreneurship and also provide implications for entrepreneurs.