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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.

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.

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.

Strategic trends towards service operations have been widely reported in the recent literature, but organisational capabilities to support such service-centred strategies are less well understood. The purpose of this paper is to identify key organisational issues in managing complex engineering service operations throughout the lifecycle.

Using instruments developed from the product lifecycle management technologies and the network configuration concept, key organisational issues for engineering service operations were identified through case studies focusing on complex engineering products and services systems across a variety of industrial sectors.

The case studies demonstrated different organisational features and strategic priorities of engineering service operations along the whole lifecycle. A generic trend has been observed for engineering systems to move from being design, development and manufacturing focused to embracing support and end-of-life recycling matters.

This paper provides an overall framework for integrating key organisational issues in engineering service operations. It contributes to the service literature by highlighting the need of developing appropriate organisational capabilities to support service-centred strategies with engineering cases. It also provides guidance for companies to manage their engineering network operations throughout the whole lifecycle of complex products and services systems.

The purpose of this study is to discuss how organizations can drive organizational performance through human capital (HC) investment through systematic thinking.

This study analyzes three companies from various industries, adopts systems thinking and uses three leading indicators from the balanced scorecard framework to explore the effects of strategic orientations for HC on innovation ecosystems and organizational performance.

In terms of academic contributions, this study broadly verifies the innovation ecosystem model for organizations and reveals that customer-oriented, internal process-oriented and innovation learning-oriented HC strategies reinforce the pathways in organizational innovation ecosystems, thereby enriching the literature on innovation ecosystems.

In terms of practical contributions, this study provides a novel HC-based perspective on developmental dynamics and details the relationships among each aspect of the innovation ecosystem and HC strategies.

The proposed architecture and strategic frameworks provide a reference for corporations to implement strategic orientations of HC, drive operations in organizational innovation ecosystems and improve organizational performance.

Understanding the computer-virus propagation is quite essential for the construction and development of anti-virus policy. While researches about the anti-virus policy have been extensively investigated, the viewpoint from sociological perspective is relatively ignored. Therefore, this paper aims to explore the dynamics of computer-virus propagation and evaluate the effectiveness of anti-virus policies through the sociological perspective.

This research constructs a virus-propagation model based on the susceptible-exposed-infective-recovered epidemic concept to simulate and explore the dynamic behavior of multipartite computer viruses through the tool of system dynamics. The effectiveness of various anti-virus policies is then evaluated via this model.

The frequency of media contact has a significant effect on the virus infection rate. The effectiveness of user self-prevention relies on the usefulness of the virus signatures. The reporting/alarm process can enhance the capability of anti-virus software company and the detected intensity of new threat. The quarantine policy can effectively reduce the spread of computer virus.

Individuals should strengthen the self-awareness of information security to reduce the negative impact. Managers should construct and implement the information security norm to regulate the behavior of staff. Anti-virus software companies should strengthen the capability of their automatic reporting/alarm mechanism to early detect the exceptional conditions and control new threats in time.

Information security management research is still in the growth phase, but it is critically important to establish the groundwork for understanding of computer viruses and the effectiveness of anti-virus policy from assorted perspectives. The major contribution of research is to explore the propagation of multipartite computer viruses and study how to prevent their destruction from the sociological and technical perspectives.

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.

This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.