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The purpose of this study is to provide a highly efficient method to obtain the kinetics of the hydrogen evolution reaction (HER) on metal electrodes in an alkaline solution and to analyze the effect of thiourea addition on HER under the same cathodic overpotential.

A novel method based on hydrogen permeation tests, potentiodynamic polarization tests and electrochemical impedance spectroscopy was put forward to characterize the HER kinetics on metal electrode.

The study found that adding thiourea accelerated the Volmer, Heyrovsky and Tafel reactions associated with HER. In addition, it reduced the hydrogen surface coverage and increased the hydrogen permeation steady-state current density. As a result, thiourea facilitated HER, promoted the diffusion of hydrogen atoms into iron and reduced the number of hydrogen atoms in the adsorbed state.

This work provides novel insights into the influence of thiourea on HER kinetics, demonstrating that thiourea addition can significantly enhance HER efficiency by altering reaction dynamics and promoting hydrogen atom diffusion into iron. This has implications for hydrogen energy applications, cathodic protection and understanding hydrogen embrittlement mechanisms.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

Waste classification plays an important role in reducing pollution, promoting waste recycling and resource utilization. This paper aims to explore the multiple reasons that affect the performance of waste classification governance.

Content analysis of the existing waste classification policies is conducted using the Latent Dirichlet Allocation (LDA) model. Based on this analysis, influencing factors are identified through the technology-organization-environment (TOE) research framework. The condition configurations and action paths that cause differences in governance performance are derived using the fuzzy-set qualitative comparative analysis method (fsQCA).

The results show that there are spatial and temporal disparities in waste classification policies among different provinces/cities. In most situations, the implementation effect of policy combinations is better than that of a single type of policy, with mandatory policies playing a key role. Additionally, a single influencing factor cannot constitute the bottleneck of high governance performance. Policy topics coordinate with environmental and technical factors to influence governance performance. Finally, in light of China's actual governance situation, several targeted implications are proposed for the practical optimization of local government waste classification governance.

This paper presents a novel approach by integrating multiple heterogeneous data sources from both online and offline channels, adopting a public-government perspective and applying the fsQCA method to investigate the combined effects of technical, organizational and environmental factors on waste classification governance performance across 31 provinces and cities in China.

This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine.

A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness.

The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss.

The optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/

This paper aims to investigate how online consumer reviews (OCRs), countdowns and self-control affect consumers' online impulse buying behavior in online group buying (OGB) and uncover the relationship between these factors.

Based on the stimulus-organism-response (SOR) framework, this research examines the effects of OCRs, countdowns and self-control on users' impulse purchases. First, the influence of emotions on impulse purchases in group purchasing is investigated. In addition, this study innovatively applies stress-coping theory to group buying research, with countdowns exerting temporal pressure on consumers and OCRs viewed as social pressure, to investigate in depth how countdowns and OCRs affect users' impulse purchase behavior. Finally, this study also surveys the moderating role of users' self-control in the impulse purchase process.

The results show that the perceived value of OCRs and positive emotions (PE) were positively correlated with impulsiveness (IMP) and the urge to buy impulsively (UBI), while negative emotions (NE) were negatively correlated with IMP. Countdowns (CD) had a positive effect on UBI. Self-control can indirectly affect users' impulse buying by negatively moderating the relationship between PE and UBI, PE and IMP and CD and UBI.

The research results can help group buying platforms and related participants understand the factors influencing users' impulse purchases in OGB and facilitate them to better design strategies to increase product sales.

Drawing upon the cognitive-affective processing system (CAPS) framework, the current study proposes a dual-pathway model that suggests self-serving leadership has a positive influence on employee knowledge hiding. The study also examines the mediating effects of relative deprivation and emotional exhaustion, as well as the moderating effect of political skill, to provide a comprehensive understanding of these relationships.

This study employed two-wave time-lagged survey data collected from 644 employees in 118 teams within a company based in Shenzhen, China. Moreover, hierarchical linear modeling (HLM) was used to test the hypothesized relationships.

The results indicated that self-serving leadership positively influenced employee knowledge hiding, and this relationship was mediated by relative deprivation and emotional exhaustion. Additionally, political skill was found to negatively moderate both the direct relationship between self-serving leadership and relative deprivation and emotional exhaustion, and the indirect path from self-serving leadership to employee knowledge hiding through relative deprivation and emotional exhaustion.

This study makes a unique contribution to the knowledge management literature in several ways. First, it introduces self-serving leadership as a predictor of employee knowledge hiding, expanding the current understanding of this phenomenon. Second, it offers a novel conceptualization, suggesting that employees coping with self-serving leadership may experience relative deprivation and emotional exhaustion, and these factors can predict their engagement in knowledge hiding. Third, the research findings on the moderating role of political skill push the boundaries of the knowledge-hiding literature, providing new insights into the conditions under which this behavior occurs.

This study aims to research the time-varying mesh stiffness (TVMS) model for orthogonal face gear drives considering elastohydrodynamic lubrication (EHL) and provide a theoretical basis for understanding the dynamic characteristics of face gear drives.

Considering EHL, a novel model is proposed to calculate the TVMS of orthogonal face gears using the deformation compatibility condition. First, the tooth surface equations of orthogonal face gears are derived according to the tooth surface generation principle. Then, the oil film thickness on the tooth surface of face gears is obtained by solving the governing equations of EHL. Furthermore, the proposed model is used to calculate the TVMS of face gears along the mesh cycle and is verified. Finally, the effects of module, tooth number of shaper cutter and pressure angle on mesh stiffness are analyzed.

The results indicate that when the contact ratio is greater than 1 and less than or equal to 2, the TVMS of face gears exhibits a phenomenon of double-single tooth alternating meshing where sudden changes occur. As the module increases, the overall mesh stiffness of face gears increases, and the magnitude of the sudden change at the moment of single-double tooth alternating meshing gradually increases. As the tooth number of shaper cutter and pressure angle increase, so does the TVMS of face gears. When the effect of oil film is considered, the calculated TVMS of face gears slightly increases overall and the increase in average oil film thickness leads to a rise in the TVMS. This study provides a theoretical basis for understanding the dynamic characteristics of face gear drives.

This study’s originality and value lie in its comprehensive approach, which includes conducting analysis based on loaded tooth contact, considering the influence of elastohydrodynamic lubrication, proposing a novel analytical–finite–element model, calculating TVMS of face gears, verifying the proposed model and analyzing the effects of typical structural parameters and oil film thickness.

Lean management has been used in various constructions around the world for more than a quarter of a century, and it is an important factor in the construction of new projects. In relation to demolition management, only standards and codes and general principles of demolition of specific buildings were evaluated. The purpose of this study is providing relation between lean management on demolition processes of municipality buildings evaluated.

This study investigates the lean demolition of demolished and renovated buildings in a metropolitan area that can be extended to all cities. In the first stage, the effective factors in the demolition of the building based on lean management were identified through a valid questionnaire based on the valid Delphi approach. Social, economic and environmental considerations were considered in designing the appropriate questionnaire.

The modified approach between the fuzzy method and partial least squares was used to evaluate important variables. All of the modified processes were developed in MATLAB by the authors of this paper. The results show that customer-focused degradation parameter has the weakest effect and waste removal variable has the most effect on lean management.

Statistical results show that there is no significant difference between the effect of lean management on variables such as demolition time, quality and type of construction (p < 0.05).

The purpose of this study is to assess the impact of Covid-19 on sustainable fashion consumption behavior by proposing a conceptual framework combining consumer-specific factors and product-specific factors with a special emphasis on consumer value perceptions.

Theory of consumption value was integrated into the knowledge, attitude behavior model in the conceptual framework having consumer-specific and product-specific aspects. Perceived value (PERVAL) scale was used to measure value perceptions. The model was verified by a survey conducted among a random sample of 520 participants. The factors were extracted by using exploratory factor analysis and then confirmed by using confirmatory factor analysis. The hypotheses in the conceptual model were tested for different consumer groups, and the strength of the relationships was calculated by using multigroup analysis in structural equation modeling.

It was observed the environmental concern raised the need for getting knowledge about the environment. The impact of environmental knowledge on the value perception of sustainable fashion products varied for the consumers affected by Covid-19 at different levels. Quality perception and price perception were influenced most by environmental knowledge in that order for the consumers with “high fear and uncertainty” and “low fear and uncertainty”. Similarly, the perceived emotional and social values were influential on purchase intention for consumers with high fear and uncertainty, whereas price and social value perceptions were influential for the consumers with low fear and uncertainty.

This study is the initial study that investigated the impact of the Covid-19 pandemic on the consumption of sustainable fashion products. The integration of theory of consumption value into the knowledge, attitude behavior model allowed identifying the relationship between environmental issues and sustainable fashion consumption. Using the PERVAL scale for measuring perceived value, the study provided valuable insights for understanding the most important value dimensions for sustainable fashion products for consumer groups affected by Covid-19 at different levels. The results regarding the changes in the rankings related to the impact of environmental knowledge on dimensions of perceived value and the impact of perceived values on purchase intention enabled the integrated model to explain the attitude–behavior gap.