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The purpose of this bibliometric study is to report research output and publications on the social determinants of health and health outcomes. This study condenses numerous studies into a single paper, which not only provides insights into the worldwide growth and advancement of the study field but also establishes a research agenda for the future.

Using the Scopus database, a thorough bibliometric study of the worldwide scientific output on social determinants of health from 2000 to 2021 was conducted. With the assistance of VOS viewer and R-based scientometric software, the worldwide development of research on social determinants of health was analysed through performance analysis and network map visualisation.

This study identified the most influential authors, studies, journals and affiliations in the field of social determinants of health, as well as the most co-cited authors and journals, based on a bibliometric analysis of 1,203 research papers retrieved from the Scopus database during the past two decades (2000–2021). Beginning in 2000 with one publication and ending in 2021, 274 articles were viewed online, relevant to the social determinants of health. From 2014 to 2021, publications continue to grow at an accelerating pace.

To the best of the authors’ knowledge, this is one of the first studies to review the social determinants of health and its outcomes. The findings of the chosen studies represent a novel and important addition to the study of social determinants of health. It offers a comprehensive bibliometric analysis of publications, in addition to the identification of vital research trends.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

The purpose of this paper is to solve the problem of adaptive predefined-time control for spacecraft rendezvous maneuver with input quantization and unknown parameters.

A significant error-shifting function is established to ensure that the limitation of the initial condition can be ignored. Then, a combination of neural networks method and minimum-learning-parameter method is used to mitigate the adverse effects caused by nonlinear system dynamics. An input quantization method is applied to improved efficiency of data communication between controller and actuator in rendezvous maneuver control system.

The simulation results verify the correctness and effectiveness of the designed control strategy which can effectively overcome the disadvantages caused by coupling nonlinear system dynamics.

This paper designs an adaptive predefined-time control method for spacecraft rendezvous maneuver control with input quantization based on backstepping control method.

This study aims to investigate how individual career management (ICM) affects career success in Chinese organizations. Leader emergence was examined through the theoretical lens of implicit leadership theory as a mediating mechanism of this relationship. In addition, leadership self-efficacy and organizational warmth were analyzed jointly as boundary conditions strengthening the relationship between ICM and leader emergence.

To avoid common method bias, the authors adopted a three-wave data collection with a one-month lagged design. A total of 765 questionnaires were distributed and 424 usable questionnaires were collected. Mplus version 8.3 was used to test the hypothesized relationships.

Findings indicated that ICM is positively related to subjective career success and objective career success via leader emergence. Moreover, leadership self-efficacy and organizational warmth jointly moderate the relationship between ICM and leader emergence.

Based on implicit leadership theory, this study identifies leader emergence as a critical mechanism explaining the positive impact of ICM on career success in the Chinese context. Lastly, results stress the simultaneous need for leadership self-efficacy and organization warmth, which can promote high-ICM employees to emerge as leaders.

This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations.

This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation.

Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent.

Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection.

The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering.

It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises.

The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.

A precise estimation of the evolutionary power spectral density (EPSD) of typhoon wind speed is a difficult and significant undertaking in the analysis of turbulence effects on large-expansive structures. A majority of the prevailing EPSD estimation techniques rely on complex signal processing methodologies, such as wavelet decomposition, Hilbert–Huang transformation and time-varying autoregressive moving average (ARMA) model. However, these approaches often pose challenges in terms of comprehensibility and practical implementation for engineers. In light of this issue, the present study introduces a straightforward and effective EPSD estimation method tailored specifically for typhoon wind speed, aiming to facilitate its understanding and application in engineering contexts.

Firstly, the mathematical model of a uniformly modulated non-stationary process is employed to represent the typhoon wind speed. Secondly, the reverse arrangement test serves as an auxiliary tool in conjunction with wavelet transform or empirical mode decomposition, aiding in the determination of the optimal slowly varying mean wind speed. Thirdly, Kernel regression technique is utilized to discern the time-dependent standard deviation of wind speed fluctuations. Finally, the power spectral density (PSD) of wind speed residuals is computed to facilitate the estimation of the EPSD.

Firstly, the reverse arrangement test-assisted approach enables the determination of an optimal time-dependent mean from the candidate results obtained through discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Secondly, the application of the Kernel regression technique facilitates accurate identification of the time-dependent variance from the fluctuating wind speed data. Thirdly, due to the influence of the extreme weather, the Gaussianity of the reduced turbulent fluctuations in typhoon wind is easily disturbed, resulting in the obvious non-Gaussian features.

This paper employs the mathematical model of uniformly modulated non-stationary process to characterize typhoon wind speeds and then proposes a straightforward and efficient method for estimating the EPSD of typhoon wind. The accuracy and efficacy of the presented estimation method are verified using the field-measured wind speed data from Typhoon Rammasun. The proposed EPSD estimation method for typhoon wind exhibits suitability for engineering applications owing to its simplicity and computational efficiency.

This paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer (ESO) is proposed. The combination of adaptive techniques, TSMC and ESO offers an effective approach for managing uncertain systems.

The dynamic model of a hypersonic vehicle is transformed into two control-oriented subsystems. The control system design incorporates an adaptive technique, an ESO and a TSMC. The ESO estimates the primary uncertainties, while the adaptive technique determines the upper limit of secondary uncertainties. These estimates are used for the design of the TSMC law. In addition, the filter is used to generate the reference trajectory to improve the dynamic performance of the system. The stability of the closed-loop system is proved by the Lyapunov stability theory.

A robust control system for hypersonic vehicles is developed with guaranteed stability and strong adaptability to various uncertainties such as parameter variations, external disturbances and actuator faults. Furthermore, the proposed system demonstrates enhanced dynamic performance compared to observer-based sliding mode control. Specifically, for the velocity and altitude tracking control, the settling time of the proposed sliding mode control is approximately 100 s and 70 s shorter than that of the observer-based sliding mode control, respectively.

Different from the single equivalent treatment, various uncertainties here are classified and treated with different strategies, which improves the disturbance rejection ability of the control system. This ability is of great significance for enhancing the autonomy, adaptability and reliability of hypersonic vehicles in extreme environments.

The imperative for sustainable energy systems is increasingly pressing as the world transitions toward renewable energy sources. Among these, triboelectric nanogenerators (TENGs) have emerged as a viable option for wind energy harvesting. However, they face significant challenges, including material durability under varying wind conditions; the intricacy of material selection and performance; and the trade-off between wear resistance and triboelectric efficiency. This study aims to address the above issues.

Herein, a mode-switch TENG (MS-TENG) was designed to overcome these limitations and serve as a self-powered energy solution for Internet of Things (IoT) sensor networks. The MS-TENG incorporates a multi-stage functional layer and an automatic mode-switching mechanism between contact and non-contact operation, thereby enhancing both efficiency and durability.

It is demonstrated that the MS-TENG achieves a maximum instantaneous output power of 0.069 mW with minimal mechanical wear, effectively capturing wind energy. Its capability to charge capacitors and power a range of electronic devices, such as temperature and humidity sensors, electronic watches and water immersion guards, underscores its practical utility across diverse settings.

This research situates the MS-TENG as a pioneering technology in smart sensor applications for future energy-harvesting endeavors, optimizing energy acquisition under fluctuating wind conditions and reinforcing the sustainability of IoT networks.

This paper aims to unveil the greenwashing intention of green bonds issuing in Chinese enterprises through the lens of stock pricing efficiency.

Drawing on data of Chinese listed companies during 2012–2021, this study uses a difference-in-differences method to study how and through what mechanisms issuing green bonds impacts stock pricing efficiency.

Issuing green bonds lowers stock pricing efficiency, verifying the greenwashing intention of green bonds in China. Potential mechanisms underlie the increased investor attention and sentiment resulting from the information disclosures about corporate green and low-carbon development. This greenwashing issue is more pronounced in firms facing lower financing constraints, having stronger relations with the government, and located in highly marketized regions. In the context of uncertainty surrounding economic policies, especially trade policies, issuing green bonds can signal a weakening of the greenwashing effect.

The quality of information disclosure should be emphasized to ensure a substantive commitment to environmental responsibility signaled by green bond issuance, thereby mitigating greenwashing concerns.

Regulators and standard-setters should improve the issuance system for green bonds and promote the sustainable development of the green bond market through formulating unified certification criteria for green bonds and implementing a stringently periodic reporting system.

First, to the best of the authors’ knowledge, it is the first study to draw on the quality of information disclosure and the perspective of stock pricing efficiency to identify whether firms issuing green bonds engage in greenwashing. Second, the study uncovers the black-box underlying this greenwashing issue through investor attention and sentiment and examines further the moderating role of economic policy uncertainties.

This investigation is designed to quantify and appraise the efficiency of resource distribution in the provision of public digital cultural services in China. By acknowledging and incorporating the realities of China's social development, the authors offer recommendations for enhancement derived from the study’s data analysis results. The research zeroes in on the dissection and analysis of the integral elements that structure the provision of public digital cultural services, and it concentrates on the associated data computation. The conclusions drawn herein are expected to serve as a significant point of reference for ongoing academic investigations and practical explorations in affiliated domains.

In this research, the authors utilize a hybrid methodology to meticulously evaluate the efficiency of the components that underpin the provision of public digital cultural services (PDCS) in China. The authors embark on deconstructing the various constituents within the PDCS supply framework, conducting in-depth analyses and providing cogent interpretations of each integral element. Subsequently, the authors deploy the well-regarded SBM super-efficiency model to ascertain the operational efficiency of these components. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.

The provision of PDCS in China as of 2021 had been characterized by overall good efficiency, significant regional disparity and a disconnect between inputs and outputs with weak correlations to economic and demographic data.

In this study, the authors provide an exhaustive deconstruction and interpretation of the public digital cultural services supply system, thereby proposing a framework for evaluating the efficiency of supply element allocation. Additionally, the authors have determined a set of distinct measurable indicators that are readily accessible for open collection. Notably, this analytical and evaluative framework designed for element analysis and measurement may also find application in efficiency evaluation research of the supply systems of other related cultural endeavors.