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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.

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.

The game strategies differ when different regions participate in the oil game. Under what circumstances will different participants choose cooperation or sanction strategies? This is the core issue of this paper.

Regarding the current and future game behavior between different regions in the oil trade, this paper constructs an evolutionary game model between two regions to explore the possibility of sanctions strategies between the two sides in different situations.

The research finds: (1) When the benefits of in-depth cooperation between the two regions are greater, both sides tend to adopt cooperative strategies. (2) When the trade conflict losses between the two regions are smaller, both sides adopt sanctions strategies. (3) When a strong region trades with a weak region, if the former adopts a sanctions strategy, the net profits are greater than the benefits of in-depth cooperation between the two regions. If the latter adopts a sanctions strategy, the net profits are less than the trade conflict losses between the two regions. There will be the strong region adopting a sanctions strategy and the weak region adopting a non-sanctions strategy. At this time, the latter should reasonably balance the immediate and future interests and give up some current interests in exchange for in-depth cooperation between the two regions. Otherwise, it will fall into the situation of unilateral sanctions by the strong against the weak.

There is no paper in the existing literature that uses the evolutionary game method to analyze the oil game problem between the two regions. This paper constructs a two-party evolutionary game model composed of crude oil importers and crude oil exporters and, based on this, analyzes the evolutionary stability between the two regions under sanctions and cooperation strategies, which enriches the energy research field.

Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs).

The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control.

It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies.

In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The traction cable is paralleled with the existing traction network of electrified railway through transverse connecting line to form the scheme of long distance power supply for the traction network. This paper aims to study the scheme composition and power supply distance (PSD) of the scheme.

Based on the structure of parallel traction network (referred to as “cable traction network (CTN)”), the power supply modes (PSMs) are divided into cable + direct PSM and cable + autotransformer (AT) PSM (including Japanese mode, French mode and new mode). Taking cable + Japanese AT PSM as an example, the scheme of long distance power supply for CTN under the PSMs of co-phase and out-of-phase power supply are designed. On the basis of establishing the equivalent circuit model and the chain circuit model of CTN, taking the train working voltage as the constraint condition, and based on the power flow calculation of multiple train loads, the calculation formula and process for determining the PSD of CTN are given. The impedance and PSD of CTN under the cable + AT PSM are simulated and analyzed, and a certain line is taken as an example to compare the scheme design.

Results show that the equivalent impedance of CTN under the cable + AT PSM is smaller, and the PSD is about 2.5 times of that under the AT PSM, which can effectively increase the PSD and the flexibility of external power supply location.

The research content can effectively improve the PSD of traction power supply system and has important reference value for the engineering application of the scheme.

The purpose of this study is to analyze the environmental efficiency level and trend of the transportation sector in the upper–mid–downstream of the Yangtze River Economic Belt and the JingJinJi region in China and assess the effectiveness of policies for protecting the low-carbon environment.

This study uses the meta-frontier slack-based measure (SBM) approach to evaluate environmental efficiency, which targets and classifies specific regions into regional groups. First, this study employs the SBM with the undesirable outputs to construct the environmental efficiency measurement models of the four regions under the meta-frontier and group frontiers, respectively. Then, this study uses the technology gap ratio to evaluate the gap between the group frontier and the meta-frontier.

The analysis reveals several key findings: (1) the JingJinJi region and the downstream of the YEB had achieved the overall optimal production technology in transportation than the other two regions; (2) significant technology gaps in environmental efficiency were observed among these four regions in China; and (3) the downstream region of the YEB exhibited the lowest levels of energy consumption and excessive CO₂ emissions.

To evaluate the differences in environmental efficiency resulting from regions and technological gaps in transportation, this study employs the meta-frontier model, which overcomes the limitation of traditional environmental efficiency methods. Furthermore, in the practical, the study provides the advantage of observing the disparities in transportation efficiency performed by the Yangtze River Economic Belt and the Beijing–Tianjin–Hebei regions.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.