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Existing studies have explored the impact of venture capital shareholding on the GEM-listed companies before and after listing from multiple perspectives. However, there has been limited research on the influence of venture capital shareholding on these companies' mergers and acquisitions(M&A) activities and performance. Additionally, two conflicting research findings have been presented in limited relevant studies. In order to clarify the mechanism by which venture capital shareholding affects M&A activities and performance of GEM-listed companies and verify existing research conclusions, this paper takes 468 M&A events completed by the acquirer of China's GEM-listed companies between 2014 and 2016 as samples to explore venture capital shareholding's effects on the M&A performance of GEM-listed enterprises. The empirical findings demonstrate that GEM-listed enterprises with venture capital shareholding perform significantly better in terms of short-term and long-term M&A performance than those without; with the increase in venture capital shareholding ratio, the short-term M&A performance of GEM-listed enterprises has remarkably improved, but the long-term M&A performance does not show obvious correlation; joint investment of venture capital can significantly improve the short-term M&A performance of GEM-listed enterprises, but it has no substantial influence on long-term M&A performance. Based on further analysis of the empirical study, it is concluded that the common one-share ownership structure of GEM-listed enterprises is not conducive to the play of the monitoring function of venture capital, and the insufficient incentives and free-riding thinking also weaken the motivation and input of some venture capital shareholders to provide value-added services. This study systematically elucidates the mechanism and impact of venture capital shareholding on the M&A performance of GEM-listed companies, addressing the shortcomings in existing research. It is conducive for GEM-listed companies to gain a rational understanding and effectively leverage the active role of venture capital shareholders in M&A activities. [ABSTRACT FROM AUTHOR]

The development of high-performance and highly stable electrocatalysts is crucial and challenging for the hydrogen evolution reaction (HER). MOFs complexed with SnS2 can form complexes with inserted structures, and their large specific surface area and numerous pores favor mass transfer, exposing the S active sites to more SnS2, improving the utilisation of the active sites and enhancing the catalytic activity for the HER. Structural collapse can be hindered to improve the stability of the HER composites. Herein, six different kinds of SnS2 were synthesized by a hydrothermal method under discrete conditions, and SI8-SnS2 was screened out to have optimal morphology and electrocatalytic performance. Two innovative SnS2@Ni-MOF and SnS2@HKUST-1 as efficient electrocatalysts were synthesized by a facile grinding method for hydrogen generation. SnS2@Ni-MOF and SnS2@HKUST-1 catalysts showed lower overpotentials of 117 mV and 142 mV at 10 mA cm−2 (η10) and smaller Tafel slopes (58 mV dec−1 and 93 mV dec−1) than six different kinds of SnS2, Ni-MOF and HKUST-1 in 0.5 M H2SO4. Meanwhile, the Cdl values for SnS2@Ni-MOF and SnS2@HKUST-1 were estimated to be 11.5 mF cm−2 and 8.0 mF cm−2, and the stabilities of SnS2, Ni-MOF, HKUST-1, SnS2@Ni-MOF, and SnS2@HKUST-1 were tested at a current density of 10 mA cm−2. The current densities of SnS2@Ni-MOF and SnS2@HKUST-1 were stable over 8 h compared to the pure samples, and SEM, XPS tests were run after the HER test, indicating that the materials have excellent electrocatalytic stability. The syntheses of SnS2@Ni-MOF and SnS2@HKUST-1 enhance their intrinsic catalytic activity, and the porous structure promotes mass transfer efficiency, thereby improving the HER performance, which will guide the development of new and promising electrocatalysts for HER. [ABSTRACT FROM AUTHOR]

Excessive fine particulate matter (PM2.5) and ozone (O3) are invisible killers affecting our wellbeing and safety, which cause great harm to people's health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM2.5 and O3 exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM2.5 and O3 pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM2.5 and O3 control in the BTH region. [ABSTRACT FROM AUTHOR]

Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This article proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. for this article are available online. [ABSTRACT FROM AUTHOR]