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Aiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.

In this paper, a support vector regression (SVR) surrogate model is constructed to solve the Sobol index. The optimal combination of SVR hyperparameters is obtained by using the improved beluga whale optimization (IBWO). Meanwhile, in order to solve the problem that Sobol sequences will form correlation regions in high-dimensional space leading to the uneven distribution of sampling points, a scrambled strategy is introduced in the Sobol sensitivity analysis using IBWO-SVR. Thus, the IBWO-SVR-SS sensitivity analysis model is established.

The results of two test functions show that the method further improves the accuracy of the sensitivity analysis. Finally, the first-order Sobol index and second-order Sobol index are solved by the IBWO-SVR-SS method using the metro bogie frame as an engineering example. Through the analysis results, the key design parameters of the frame and the design parameter combinations with more obvious coupling relationships are identified, providing a strong reference for the subsequent analysis and structural optimization.

Sobol sensitivity analysis using the surrogate model method can effectively improve the efficiency of the solution. In addition, IBWO is used for the optimization of the SVR hyperparameters to improve the accuracy and efficiency of the optimization, and finally, the correction of the Sobol sequence through the introduction of the disruption strategy also further improves the accuracy of the sensitivity analysis of Sobol.

This study aims to investigate the formation and characteristics of Taylor bubbles resulting from short-time gas injection in liquid-conveying pipelines. Understanding these characteristics is crucial for optimizing pipeline efficiency and enhancing production safety.

The authors conducted short-time gas injection experiments in a vertical rectangular pipe, focusing on Taylor bubble formation time and stable length. Computational fluid dynamics simulations using large eddy simulation and volume of fluid models were used to complement the experiments.

Results reveal that the stable length of Taylor bubbles is significantly influenced by gas injection velocity and duration. Specifically, high injection velocity and duration lead to increased bubble aggregation and recirculation region capture, extending the stable length. Additionally, a higher injection velocity accelerates reaching the critical local gas volume fraction, thereby reducing formation time. The developed fitting formulas for stable length and formation time show good agreement with experimental data, with average errors of 6.5% and 7.39%, respectively. The predicted values of the formulas in glycerol-water and ethanol solutions are also in good agreement with the simulation results.

This research provides new insights into Taylor bubble dynamics under short-time gas injection, offering predictive formulas for bubble formation time and stable length. These findings are valuable for optimizing industrial pipeline designs and mitigating potential safety issues.

This article intends to explore how organizational learning affects innovation in public library services, and the role of public librarians in innovation in library services.

This study collected 375 valid questionnaires from 19 public libraries in Shanghai and Zhejiang based on organizational learning, organizational innovation and employee psychological empowerment theory. Additionally, SPSS and HLM are used to analyze the relationship among the three processes of organizational learning: knowledge acquisition, knowledge sharing and knowledge application, and public library service innovation.

Results show that organizational learning has a significant positive effect on the service innovation of public libraries. Knowledge acquisition and knowledge application in the process of organizational learning have a significant positive influence on the service innovation of public libraries, but the impact of knowledge sharing on service innovation is weak. Employee psychological empowerment has a negative regulating influence on knowledge sharing–public library service innovation, but no significant influence on knowledge application–public library service innovation and knowledge acquisition–public library service innovation.

This research explores the effectiveness of the theory of organizational learning in the field of public libraries and also confirms the role of librarians in the work of public libraries. Together, they promote the innovation of public libraries.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

The purpose of this study is to present a bibliometric and network analysis that uses the Scopus and Dimension databases to provide new insights into the progression toward the study of sustainable economic development.

This analysis has been drawn on 665 papers published between 2015 and 2023. Bibliometric analysis characterizes a research topic by identifying leading nations, the most significant authors and expressive publications. Network analysis revealed keyword evolution over time, co-citation patterns and study grouping. Content analysis was used to identify major topic in the discipline, with a focus on their interrelationships. Each publication in the data set is briefly described, along with its methodological approach.

The results of this study show that green finance plays a major role in long-term economic growth, having a significant influence on the preservation of environmental quality, economic efficacy and a more comprehensive economic system. Financial technology also accelerates the transition to a carbon-neutral economy by enhancing the beneficial effects of green finance on aspects of the economic system and environmental conservation.

The investigation is based only on Scopus and Dimensions-indexed journal articles. However, additional studies should incorporate publications from other reputable databases, such as Web of Science, PubMed and Science Direct, for the bibliometric analysis, so that the findings of the model analysis become more reliable and valid with examination of more documents. The visualization of similarity viewer was used for data analysis in the study, there is a scope for using other tools such as Biblioshiney and CitNet Explorer.

To support long-term economic growth, authorities should encourage Fintech companies to actively participate in various green finance initiatives and environmental conservation businesses. Financial managers should facilitate the integration of technology and green finance for financial services. It is important to encourage institutional and individual investors alike to look into more environmentally friendly ways to invest and save money. Policymakers should provide a platform for global awareness and government agencies should enhance their recommendations to state governments to increase the efficacy of green finance.

This study contributes to the literature by investigating the relationship between Fintech and green financing. This study holds significance for financial intermediaries, industrialists, investors and policymakers by providing insights into the integration of Fintech with green finance for sustainable development. These findings affirm the pivotal role of Fintech and green finance in fostering sustainable economic development. The novelty of the topic and the variety of publications in which it has been published demonstrate that sustainable economic development has piqued the interest of a wide range of areas.