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BackgroundThe application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework.ResultsThe results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education.ConclusionsThe application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.

Educational robotics, as emerging technologies, have been widely applied in the field of STEM education to enhance the instructional and learning quality. Although previous research has highlighted potentials of applying educational robotics in STEM education, there is a lack of empirical evidence to investigate and understand the overall effects of using educational robotics in STEM education as well as the critical factors that influence the effects. To fill this gap, this research conducted a multilevel meta-analysis to examine the overall effect size of using educational robotics in STEM education under K-16 education based on 30 effect sizes from 21 studies published between 2010 and 2022. Furthermore, we examined the possible moderator variables of robot-assisted STEM education, including discipline, educational level, instructor support, instructional strategy, interactive type, intervention duration, robotic type, and control group condition. Results showed that educational robotics had the moderate-sized effects on students’ STEM learning compared to the non-robotics condition. Specifically, educational robotics had moderate-sized effects on students’ learning performances and learning attitudes, and insignificant effects on the improvement of computational thinking. Furthermore, we examined the influence of moderator variables in robot-assisted STEM education. Results indicated that the moderator variable of discipline was significantly associated with the effects of educational robotics on STEM learning. Based on the findings, educational and technological implications were provided to guide future research and practice in the application of educational robotics in STEM education.

The application of punitive damages in intellectual property cases in China has encountered notable deficiencies within the judicial context. These deficiencies, including a limited range of applicability, frequent recourse to statutory damages, difficulties in ascertaining the appropriate damages base, and a marked reliance on subjective factors in calculating multipliers, undermine judicial consistency and fairness. In this study, we conducted a comprehensive quantitative analysis by selecting 79 pertinent judgments from a dataset of 3,478 intellectual property rulings. Initially, we developed a multiple linear regression model to assess punitive damages. Upon encountering negative estimated coefficients, we improved the model to a Beta Generalized Linear Model. The efficacy of the new model was validated through an improved R-squared value compared to the original model and by analyzing an additional 45 cases. Both models highlight the scope of infringement and the reputation of intellectual property assets as critical variables. They suggest that wider infringements lead to greater economic penalties and emphasize the significant market value and potential financial losses of intellectual property. Significantly, this study represents a pioneering initiative in China, establishing a model to assist in the legal determination of punitive damages in IP cases.

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

There is growing interest in the impact of climate change on agricultural labor supply in China, rigorous empirical evidence for this issue is insufficient. This potentially important channel through which climate change may affect agricultural labor supply has not received attention. Using a panel survey data of 100 administrative villages and 2977 farmers in China, we find that temperature and precipitation do affect farmers’ labor allocation, 1°C increase from the current average temperature will reduce agricultural labor supply by 0.252%, and 1mm increase from the current average rainfall will reduce agricultural labor supply by 0.001%. Climate change also leads to the decline of net agricultural income, which creates distorted incentives for households to over-supply labor to non-agriculture. Moreover, farmers with relatively lower risk tolerance preferred to reduce the current supply of agricultural labor when net agricultural income is projected to decrease under climate change scenarios.

pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in northern China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol-phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species, and inferred pH values exhibit a bimodal distribution, with peaks between −2 and 2 and between 7 and 10, depending on whether anions or cations are in excess. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by these measurement errors. In future studies, the reverse mode should be avoided whereas the forward mode should be used. Forward-mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in northern China winter haze, indicating further that ammonia plays an important role in determining this property. The assumed particle phase state, either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions. The unrealistic pH values of about 7 in a few previous studies (using the standard ISORROPIA model and stable state assumption) resulted from coding errors in the model, which have been identified and fixed in this study.

Severe events of wintertime particulate air pollution in Beijing (winter haze) are associated with high relative humidity (RH) and fast production of particulate sulfate from the oxidation of sulfur dioxide (SO 2 ) emitted by coal combustion. There has been considerable debate regarding the mechanism for SO 2 oxidation. Here we show evidence from field observations of a haze event that rapid oxidation of SO 2 by nitrogen dioxide (NO 2 ) and nitrous acid (HONO) takes place, the latter producing nitrous oxide (N 2 O). Sulfate shifts to larger particle sizes during the event, indicative of fog/cloud processing. Fog and cloud readily form under winter haze conditions, leading to high liquid water contents with high pH (>5.5) from elevated ammonia. Such conditions enable fast aqueous-phase oxidation of SO 2 by NO 2 , producing HONO which can in turn oxidize SO 2 to yield N 2 O.This mechanism could provide an explanation for sulfate formation under some winter haze conditions. How sulfur dioxide emitted through coal combustion is oxidized to sulfate particles during winter haze pollution events has been the subject of debate. Here, the authors show that rapid oxidation takes place by nitrogen dioxide and nitrous acid, producing nitrous oxide together with sulfate.

We investigate the rapid formation and evolutionary mechanisms of an extremely severe and persistent haze episode that occurred in northern China during winter 2015 using comprehensive ground and vertical measurements, along with receptor and dispersion model analysis. Our results indicate that the life cycle of a severe winter haze episode typically consists of four stages: (1) rapid formation initiated by sudden changes in meteorological parameters and synchronous increases in most aerosol species, (2) persistent evolution with relatively constant variations in secondary inorganic aerosols and secondary organic aerosols, (3) further evolution associated with fog processing and significantly enhanced sulfate levels and (4) clearing due to dry, cold north-northwesterly winds. Aerosol composition showed substantial changes during the formation and evolution of the haze episode but was generally dominated by regional secondary aerosols (53–67%). Our results demonstrate the important role of regional transport, largely from the southwest but also from the east and of coal combustion emissions for winter haze formation in Beijing. Also, we observed an important downward mixing pathway during the severe haze in 2015 that can lead to rapid increases in certain aerosol species.

The chemical mechanisms responsible for rapid sulfate production, an important driver of winter haze formation in northern China, remain unclear. Here, we propose a potentially important heterogeneous hydroxymethanesulfonate (HMS) chemical mechanism. Through analyzing field measurements with aerosol mass spectrometry, we show evidence for a possible significant existence in haze aerosols of organosulfur primarily as HMS, misidentified as sulfate in previous observations. We estimate that HMS can account for up to about one-third of the sulfate concentrations unexplained by current air quality models. Heterogeneous production of HMS by SO2 and formaldehyde is favored under northern China winter haze conditions due to high aerosol water content, moderately acidic pH values, high gaseous precursor levels, and low temperature. These analyses identify an unappreciated importance of formaldehyde in secondary aerosol formation and call for more research on sources and on the chemistry of formaldehyde in northern China winter.

Based on the extreme value theory, copula function, and conditional value at risk (Abbreviated as CoVaR) model, an extreme value copula CoVaR (EV-Copula CoVaR) model is established. In application, the risk spillover effect of the carbon trading market on the stock market of China is investigated. Firstly, using the index synthesis method, the carbon trading price index is synthesized through the price data of the test area of carbon emission, then the risk spillover effect of the carbon market is measured by the EV-Copula CoVaR, and the dynamic risk spillover ΔCoVaR of the carbon market to each stock market is investigated. Finally, the downside ΔCoVaR under different significance levels is measured, and the relationship between the self-risk and spillover risk of the carbon market is explored, the largest risk spillover effect to the stock market is the electricity market. The smaller the significance level, the greater the carbon market self-risk, and the greater the risk spillover of the carbon market to the stock market, which shows that there is a positive correlation between them.