Mo2C-Based Ceramic Electrode with High Stability and Catalytic Activity for Hydrogen Evolution Reaction at High Current Density

Developing robust electrodes with high catalytic performance is a key step for expanding practical HER (hydrogen evolution reaction) applications. This paper reports on novel porous Mo2C-based ceramics with oriented finger-like holes directly used as self-supported HER electrodes. Due to the suitable MoO3 sintering additive, high-strength (55 ± 6 MPa) ceramic substrates and a highly active catalytic layer are produced in one step. The in situ reaction between MoO3 and Mo2C enabled the introduction of O in the Mo2C crystal lattice and the formation of Mo2C(O)/MoO2 heterostructures. The optimal Mo2C-based electrode displayed an overpotential of 333 and 212 mV at 70 °C under a high current intensity of 1500 mA cm-2 in 0.5 m H2SO4 and 1.0 m KOH, respectively, which are markedly better than the performance of Pt wire electrode; furthermore, its price is three orders of magnitude lower than Pt. The chronopotentiometric curves recorded in the 50 - 1500 mA cm-2 range, confirmed its excellent long-term stability in acidic and alkaline media for more than 260 h. Density functional theory (DFT) calculations showed that the Mo2C(O)/MoO2 heterostructures has an optimum electronic structure with appropriate *H adsorption-free energy in an acidic medium and minimum water dissociation energy barrier in an alkaline medium.

Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm

Forecasting China's carbon price accurately can encourage investors and manufacturing industries to take quantitative investments and emission reduction decisions effectively. The inspiration for this paper is developing an error-corrected carbon price forecasting model integrated fuzzy dispersion entropy and deep learning paradigm, named ICEEMDAN-FDE-VMD-PSO-LSTM-EC. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to primary decompose the original carbon price. Subsequently, the fuzzy dispersion entropy (FDE) is conducted to identify the high-complexity signals. Thirdly, the variational mode decomposition (VMD) and deep learning paradigm of particle swarm optimized long short-term memory (PSO-LSTM) models are employed to secondary decompose the high-complexity signals and perform out-of-sample forecasting. Finally, the error-corrected (EC) method is conducted to re-modify and strengthen the above-predicted accuracy. The results conclude that the forecasting performance of ICEEMDAN-type secondary decomposition models is significantly better than the primary decomposition models, the deep learning PSO-LSTM-type models have superiority in forecasting China carbon price, and the EC method for improving the forecasting accuracy has been proved. Noteworthy, the proposed model presents the best forecasting accuracy, with the forecasting errors RMSE, MAE, MAPE, and Pearson's correlation are 0.0877, 0.0407, 0.0009, and 0.9998, respectively. Especially, the long-term forecasting ability for 750 consecutive trading prices is outstanding. Those conclusions contribute to judging the carbon price characteristics and formulating market regulations.

Unraveling the contributions of prosodic patterns and individual traits on cross-linguistic perception of Spanish sentence modality

Cross-linguistic perception is known to be molded by native and second language (L2) experiences. Yet, the role of prosodic patterns and individual characteristics on how speakers of tonal languages perceive L2 Spanish sentence modalities remains relatively underexplored. This study addresses the gap by analyzing the auditory performance of 75 Mandarin speakers with varying levels of Spanish proficiency. The experiment consisted of four parts: the first three collected sociolinguistic profiles and assessed participants' pragmatic competence and musical abilities. The last part involved an auditory gating task, where participants were asked to identify Spanish broad focus statements and information-seeking yes/no questions with different stress patterns. Results indicated that the shape of intonation contours and the position of the final stressed syllable significantly impact learners' perceptual accuracy, with effects modulated by utterance length and L2 proficiency. Moreover, individual differences in pragmatic and musical competence were found to refine auditory and cognitive processing in Mandarin learners, thereby influencing their ability to discriminate question-statement contrasts. These findings reveal the complex interplay between prosodic and individual variations in L2 speech perception, providing novel insights into how speakers of tonal languages process intonation in a non-native Romance language like Spanish.

Corporate social responsibility initiatives and their role in firms' reputation and green economic recovery through organizational trust

Maintaining operations in the face of crises like COVID-19 is difficult. Using the stakeholder theory, this study examines the impact of corporate social responsibility (CSR) programs targeting company employees. Their social position and the likelihood of a green economic rebound (GER) are evaluated. Evidence shows that employee-focused CSR activities implemented by tourism boost organizational GER by fostering a more trusting work environment for their staff. Management and non-management staff at Chinese Tourism were polled using a non-probabilistic convenience sample and a 5-point Likert scale. Structured equation modeling was used to conduct structural analyses. Employee-focused CSR is a significant predictor of a firm reputation in the Chinese tourism industry. In addition, it is found that trust inside the organization acts as a go-between. The evidence also supports the hypothesis that a company's rising profile triggers GER. This research delves deeply into the connection between employees' perceptions of a company's employee-focused CSR initiatives, that company's reputation in the community, and employees' general enthusiasm for their job, a group that has been understudied until now. The findings are helpful for tourism management because they show them how to employ employee-focused CSR activities to strengthen connections with internal stakeholders while also using that reputation to shift to a greener way of doing business.

Effect of whole-body vibration on neuromuscular activation and explosive power of lower limb: A systematic review and meta-analysis

OBJECTIVE

The review aimed to investigate the effects of whole-body vibration (WBV) on neuromuscular activation and explosive power.

METHODS

Keywords related to whole-body vibration, neuromuscular activation and explosive power were used to search four databases (PubMed, Web of Science, Google Scholar and EBSCO-MEDLINE) for relevant studies published between January 2000 and August 2021. The methodology of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used. The eligibility criteria for the meta-analysis were based on PICOST principles. Methodological assessment used the Cochrane scale. Heterogeneity and publication bias were assessed by I2 index and funnel plots, respectively. The WBV training cycle is a random effect model. Publication bias was also assessed based on funnel plots. This study was registered in PROSPERO (CRD42021279439).

RESULTS

A total of 156 participants data in 18 studies met the criteria and were included in the meta-analysis for quantitative synthesis. Results of the meta-analysis showed significant improvements in lower limb neuromuscular activation immediately after WBV compared with the baseline (SMD = 0.51; 95% CI: 0.26, 0.76; p<0.001), and no significant heterogeneity was observed (I2 = 38%, p = 0.07). In addition, the highest increase in lower limb explosive power was observed (SMD = 0.32; 95% CI: 0.11, 0.52; p = 0.002), and no significant heterogeneity (I2 = 0%, p = 0.80) was noted.

CONCLUSIONS

WBV training could improve neuromuscular activation and explosive power of the lower limb. However, due to different vibration conditions, further research should be conducted to determine standardized protocols targeting performance improvement in athletes and healthy personnel experienced in training.

Revealing the transmission mechanism and spatial spillover of carbon emission reduction caused by high-speed rail opening

In the context of China’s commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, as well as its strategy to build a strong transportation country, it is of foremost importance to study the carbon emission reduction effect of the opening of high-speed rail (HSR). This paper innovatively introduces the frequency of HSR stops as an indicator of HSR operation, and uses a time-varying difference-in-difference (DID) model, a mediating effect model and a spatial DID model to assess the direct and indirect impact, transmission mechanism, and spatial spillover effects of the opening and operation of HSR on carbon emission reduction based on a panel of 279 prefecture-level cities from 2003 to 2017. We found that the opening and operation of HSR significantly reduced urban carbon emissions. The direct transmission mechanism analysis shows that the opening of HSR can reduce carbon emissions by replacing highway passenger traffic. Indirect mechanism analysis shows that the opening of HSR can reduce carbon emissions through technological effect, structural effect and opening effect. The test of spatial spillover effect shows that the opening of HSR can promote carbon emission reduction not only in node cities, but also in neighboring cities.