China University of Labor Relations

ABSTRACT To reveal the macroscopic explosion characteristics and gas-solid product characteristics of different coal dust samples, coal dust explosion experiments were conducted in a 20 L spherical vessel. The explosion characteristic parameters, such as the maximum explosion pressure (P ex) and the maximum pressure rise rate (dP/dt)ex were recorded. Meanwhile, the post-explosion coal dust samples and gaseous products were collected and analyzed. The research efforts include gas chromatography analysis of the gaseous products, proximate analysis, and SEM analysis of coal dust samples before and after explosions. The results show that the P ex and (dP/dt)ex initially exhibit a gradual decrease with increasing vitrinite reflectance (R 0), and an increasing trend is observed between medium metamorphic degree coal and high-rank coal. Furthermore, P ex exhibits an initial increase and then a decrease in trend with the rise in coal dust concentration. Proximate analysis indicated that the content of volatile matter and fixed carbon decreases before and after coal dust explosions, while the content of moisture and ash increases. From a microscopic perspective, the microstructure of coal dust transitions from a smooth, block-like structure to a rough, porous configuration before and after an explosion. The residual gas composition analysis reveals that the oxygen concentration dropped below 10% after the coal dust explosion. When the concentration of coal dust is low, the main gaseous products are CO2, with a relatively low amount of CO and CH4, and the gases C2H6, C2H2, and C2H4 are nearly undetectable. However, the concentration of CO and hydrocarbon gases gradually increases at higher coal dust concentrations.

Urban forest parks play a vital role in promoting physical activities (PAs) and providing cultural ecosystem services (CESs) that enhance citizens’ well-being. This study aims to reevaluate CESs by focusing on the physical activity experiences of park visitors to optimize park management and enhance citizen satisfaction. This study utilized social media data and employed natural language processing techniques and text analysis tools to examine experiences related to physical activities in Beijing Olympic Forest Park, Xishan Forest Park, and Beigong Forest Park. A specialized sports activity dictionary was developed to filter and analyze comments related to PA, emphasizing the impact of natural environments on enjoyment and participation in PA. The importance–performance analysis (IPA) method was used to assess the service characteristics of each park. The findings reveal that urban forest parks are highly valued by citizens, particularly for their natural landscapes, leisure activities, and the emotional fulfillment derived from PA, with 82.58% of comments expressing positive sentiments. Notably, appreciation for natural landscapes was exceptionally high, as evidenced by the frequent mentions of key terms such as ‘scenery’ (mentioned 2871 times), ‘autumn’ (mentioned 2314 times), and ‘forest’ (mentioned 1439 times), which significantly influence park usage. However, 17.11% of the reviews highlighted dissatisfaction, primarily with the management of facilities and services during sports and cultural activities. These insights underscore the need for performance improvements in ecological environments and sports facilities. This study provides a novel perspective on assessing and optimizing urban forest parks’ functions, particularly in supporting active physical engagement. The rich CESs offered by these parks enhance physical activity experiences and overall satisfaction. The findings offer strategic insights for park managers to better meet citizens’ needs and improve park functionality.

Forests play a vital role in atmospheric CO2 sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R2 and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%.

Navigating the nuanced realm of app-work, the current research draws from the Job Demands-Resources (JD-R) model, emphasizing not just job demands but also the untapped job resources embedded in algorithmic control. Our investigation then delves into how perceived algorithmic control (PAC) influences the multitasking performance of delivery riders in the gig economy. First, we developed and validated the PAC scale following rigorous methodological approaches. Second, through a 7-day diary survey of 102 Chinese delivery riders, we elucidated the mediating role of job crafting in the positive relationship between PAC and delivery riders’ multitasking performance. Moreover, we found that personal-level resources (i.e., proactive personality) and technological-level resources (i.e., algorithmic transparency) serve as vital catalysts that amplify the constructive pathway from PAC to job crafting. Our research reveals the deep-seated implications of algorithmic control on app-workers’ work outcomes and illuminates the conditional and multifaceted impacts of individual and algorithmic attributes. Implications, limitations, and future research trajectories are further discussed.

Generative artificial intelligence (AI) has gained popularity for its ability to revolution work, business, and significantly improve the efficiency of human-AI interaction. Characterized as possessing human-like abilities and minds, generative AI has drawn much attention among scholars and practitioners. This article explores the following research question: “how perceived mind (i.e, perceived AI morality) and perceived substitutive role (i.e., AI awareness) of generative AI impact the employees’ unethical behavior using generative AI?” Drawing upon the literature of anthropomorphism and social schemas, we propose when individuals perceive high levels of morality of generative AI, they are more willing to pursuit a responsible use of it. We tested our research model in a three-wave survey of 641 employees. The results demonstrated that perceived AI morality decreased employees’ propensity to make moral disengagement, leading to the decline of their unethical behavior using generative AI. Besides, individuals with low level of AI awareness or did not regard the generative AI as a substitutive role are less inclined to make moral disengagement and then engage less in unethical behavior using generative AI. These findings have implications for theory and practice concerning employees’ responsible use of generative AI.