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Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method.

Regression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items.

Some interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information.

The proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining.

Compared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations.

The proposed algorithms provide a novelty view to define and mine association.

This study aims to assess the level of development of the digital economy by constructing a comprehensive measurement system. It explores regional differences within China’s digital economy, highlighting the varying degrees of digital infrastructure, industrialization, governance and innovation capabilities across provinces.

A multidimensional analytical framework including digital infrastructure, industrialization, digitization, governance and innovation was developed. Entropy methods were used to calculate the weights of each dimension. The coupled coordination degree model and the Tobit model with random effects panel are applied to analyze the current situation, discrepancies and influencing factors.

This study reveals significant regional differences in the development of China’s digital economy, characterized by a pattern of “strong in the east, weak in the west; high in the south, low in the north.” This geographical imbalance exacerbates the “polarization effect” and the “siphon effect,” where resources and growth tend to concentrate in already developed areas, further intensifying regional inequalities. The development of the digital economy is driven by principles of innovation, coordination and sharing, which facilitate the creation and dissemination of new technologies and collaboration across different sectors. However, this progress is also constrained by considerations of environmental sustainability (green) and economic openness.

This paper contributes to the body of knowledge by providing a novel multidimensional measurement system for the level of digital economy development. The unique application of the coupled coordination degree model and Tobit model to analyze regional differences and influencing factors provides insights into the dynamics of China’s digital economy.

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.

The purpose of this paper is to establish a maturity evaluation model for the application of construction steel structure welding robotics suitable for the actual situation and specific characteristics of engineering projects in China and then to assess the maturity level of the technology in the application of domestic engineering projects more scientifically.

The research follows a qualitative and quantitative analysis method. In the first stage, the structure of the maturity model is constructed and the evaluation index system is designed by using the ideas of the capability maturity model and WSR methodology for reference. In the second stage, the design of the evaluation process and the selection of evaluation methods (analytic hierarchy process method, multi-level gray comprehensive evaluation method). In the third stage, the data are collected and organized (preparation of questionnaires, distribution of questionnaires, questionnaire collection). In the fourth stage, the established maturity evaluation model is used to analyze the data.

The evaluation model established by using multi-level gray theory can effectively transform various complex indicators into an intuitive maturity level or score status. The conclusion shows that the application maturity of building steel structure welding robot technology in this project is at the development level as a whole. The maturity levels of “WuLi – ShiLi – RenLi” are respectively: development level, development level, between starting level and development level. Comparison of maturity evaluation values of five important factors (from high to low): environmental factors, technical factors, management factors, benefit factors, personnel and group factors.

In this paper, based on the existing research related to construction steel structure welding robot technology, a quantitative and holistic evaluation of the application of construction steel structure welding robot technology in domestic engineering projects is conducted for the first time from a project perspective by designing a maturity evaluation index system and establishing a maturity evaluation model. This research will help the project team to evaluate the application level (maturity) of the welding robot in the actual project, identify the shortcomings and defects of the application of this technology, then improve the weak links pertinently, and finally realize the gradual improvement of the overall application level of welding robot technology for building steel structure.

Continuum robots offer unique advantages in various specialized environments, particularly in confined or hard-to-reach spaces. Inverse kinematics and real-time shape estimation constitute crucial aspects of closed-loop control for continuum robots, presenting challenging problems. This paper aims to present an inverse kinematics and shape reconstruction method, which relies solely on the knowledge of base and end positions and orientations.

Based on the constant curvature assumption, continuum robots are regarded as spatial curves composed of circular arcs. Using geometric relationships, the mathematical relationships between the arc chords, points on the bisecting plane and the coordinate axes are established. On this basis, the analytical solution of the inverse kinematics of the continuum robots is derived. Using the positions and orientations of the base and end of the continuum robots, the Levenberg–Marquardt algorithm is used to solve the positions of the cubic Bezier curves, and a new method of spatial shape reconstruction of continuum robots is proposed.

The inverse kinematics and spatial shape reconstruction simulation of the continuum robot are carried out, and the spatial shape measurement experimental platform for the continuum robot is constructed to compare the measured and reconstructed spatial shapes. The results show that the maximum relative error between the actual shape and the reconstructed shape of the continuum robot is 2.08%, which verifies the inverse kinematics and shape reconstruction model. Additionally, when the bending angle of a single bending section of the continuum robot is less than 135°, the shape reconstruction accuracy is higher.

The proposed inverse kinematics solution method avoids iterative solving, and the shape reconstruction model does not rely on mechanical models. It has the advantages of being simple to solve, highly accurate and fast in computation, making it suitable for real-time control of continuum robots.