Beijing Normal University

Long working hours continue to pose a challenge for a considerable number of employees today. Departing from the predominant focus on the detrimental consequences associated with personal overtime work, this study aims to investigate the influences of perceived coworkers working overtime (PCWO) on employees. We theorised that PCWO may constitute a type of stressful event for employees and proposed that it will lead to employees' daily withdrawal responses (i.e. time banditry behaviour and turnover intention) through an increase in negative affect (NA). In addition, employees' workaholism may have a cross-level impact on the within-individual level relationships between their NA and withdrawal responses. We tested our theoretical hypotheses using the experience sampling methodology (ESM), with 111 full-time employees reporting their working experiences over 10 workdays. Results from the multilevel analysis revealed that even after controlling for one's own working hours, PCWO was positively related to employees' NA, which, in turn, led to an increase in time banditry behaviour and turnover intention. In addition, we also found that the within-individual level relationship between NA and turnover intention was stronger for employees higher

As artificial intelligence (AI) continues to advance, recent developments of Generative AI (GenAI) have sparked great interest, posing questions for policymakers, technology innovators, educators, and EdTech researchers about possible paradigm changes for education. This chapter critically examines the development of Artificial Intelligence in Education, which promised to revolutionise educational practices by providing effective, personalised, learning at scale—first through rudimentary teaching machines and subsequently via advanced adaptive learning systems. We argue that Adaptive Intelligent Tutoring Systems (ITS) reinforce the content delivery model and restrict pedagogic opportunities in teaching and learning when adapted to existing educational models. The chapter examines the relevance and value of existing theories of learning in the development of educational technology and a need for new theories when an AI agent becomes an active partner in teaching and learning process and discusses the complexity of educational innovation from interdisciplinary perspectives. Finally, we offer an analytical assessment of the opportunities and limitations of GenAI in education using the cybernetic principle of variety, and propose a framework to address organisational, pedagogical, and technological challenges for using GenAI to support new formal learning and pedagogical practices.

Generative AI, exemplified by models, such as ChatGPT, Gemini, and Midjourney, carry transformative potential for educational personalisation and inclusion. Yet, its integration into educational systems raises concerns about its ethical use, plagiarism, and bias. In this chapter, we explain what generative AI is, how it functions and discuss how it can be integrated into educational programmes and practices. We advocate for approaches that are human-centred, where educators and learners have direct access to and are encouraged to use generative AI models to develop generative AI literacy–learning by doing . Although it is a challenging mode of integration, we set out an approach for researching the ethical and effective integration of generative AI in education. This involves conducting participatory transdisciplinary research using a design-based research methodology (DBR). This is to facilitate iterative but sustainable transformation in educational practices, advocating for systemic change over superficial adoption.

The efficiency of nitrate reduction reaction (NO3RR) at low nitrate concentration is predominantly hindered by the poor affinity of nitrate ions and competitive hydrogen evolution reaction (HER), particularly in neutral and acidic media. Here, an innovative strategy to leverage the interfacial electric field (IEF) is introduced to boost the NO3RR performance. By in situ constructing tannic acid‐metal ion (TA‐M²⁺) crosslinked structure on the electrode surface, the TA‐M²⁺‐CuO NW/Cu foam sample exhibits an exceptional Faraday efficiency of 99.4% at −0.2 V versus reversible hydrogen electrode (RHE) and 83.9% at 0.0 V versus RHE under neutral and acidic conditions, respectively. The computational studies unveil that the TA‐Cu²⁺ complex on the CuO (111) plane induces the increasing concentration of nitrate at the interface, accelerating NO3RR kinetics over HER via the IEF effect. This interfacial modulation strategy also contributes the enhanced ammonia production performance when it is employed on commercial electrode materials and flow reactors, exhibiting great potential in practical application. Overall, combined results illustrated multiple merits of the IEF effect, paving the way for future commercialization of NO3RR in the ammonia production industry.

Containers have gained popularity in Edge Computing (EC) networks due to their lightweight and flexible deployment advantage. In resource-constrained EC environments, overbooking container resources can substantially improve resource utilization. However, existing work overlooks the complex interplay between resource provisioning and container scheduling, which may result in performance degradation or inefficient resource utilization due to highly dynamic resource heterogeneity in EC. To address this issue, this paper presents a novel joint Resource Overbooking and Container Scheduling (ROCS) algorithm. Our approach accounts for resource heterogeneity and the geographical distribution of edge nodes, and we formulate the ROCS problem to consolidate various costs and revenues into a single profit metric for service providers. To enhance resource utilization and maximize the profit of the service providers, we develop an efficient algorithm that operates within a hybrid action space scheme by leveraging soft actor-critic reinforcement learning. Furthermore, we introduce a risk assessment mechanism to mitigate overbooking risks. Large-scale simulations with real-world data traces demonstrate the efficacy of our proposed ROCS algorithm, validating its advantage of improving resource utilization within EC networks.

Extending serverless computing to the edge has emerged as a promising approach to support service, but startup containerized serverless functions lead to the cold-start delay. Recent research has introduced container caching methods to alleviate the cold-start delay, including cache as the entire container or the Zygote container. However, container caching incurs memory costs. The system must ensure fast function startup and low memory cost of edge servers, which has been overlooked in the literature. This paper aims to jointly optimize startup delay and memory cost. We formulate an online joint optimization problem that encompasses container scheduling decisions, including invocation distribution, container startup, and container caching. To solve the problem, we propose an online algorithm with a competitive ratio and low computational complexity. The proposed algorithm decomposes the problem into two subproblems and solves them sequentially. Each container is assigned a randomized strategy, and these container-level decisions are merged to constitute overall container caching decisions. Furthermore, a greedy-based subroutine is designed to solve the subproblem associated with invocation distribution and container startup decisions. Experiments on the real-world dataset indicate that the algorithm can reduce average startup delay by up to 23% and lower memory costs by up to 15%.

Finding an optimal subset of nodes or links to disintegrate harmful networks is a fundamental problem in network science, with potential applications to anti-terrorism, epidemic control, and many other fields of study. The challenge of the network disintegration problem is to balance the effectiveness and efficiency of strategies. In this article, we propose a cost-effective targeted enumeration (TE) method for network disintegration. The proposed approach includes two stages: 1) searching for candidate objects and 2) identifying an optimal solution. In the first stage, we use rank aggregation to generate a comprehensive ranking of node importance, upon which we identify a small-scale candidate set of nodes to remove. In the second stage, we use an enumeration method to find an optimal combination among the candidate nodes. Extensive experimental results on synthetic and real-world networks demonstrate that the proposed method achieves a satisfying tradeoff between effectiveness and efficiency. Our adaptable TE approach can effectively address a range of combinatorial optimization challenges with significant potential applications, including personnel recruitment, portfolio management, and pharmaceutical development.

Fuzzy Cognitive Maps (FCMs) are directed graphs with multiple nodes, rendering them well-suited for tackling the challenges of multivariate time series (MTS) forecasting. However, the conventional FCMs encounter obstacles in long-term forecasting, primarily due to the cumulated errors arising from iterative one-step forecasting. Drawing inspiration from recent advancements on fuzzy information granulation, this paper introduces a novel trend fuzzy granulation-based three-layer FCM model that operates at a granular level, effectively addressing abovementioned obstacles. This model leverages an optimization algorithm to determine the optimal number of granules for granulating an MTS into a granular time series (GTS), enabling the simultaneous consideration of trend information across various dimensions of the given MTS. Subsequently, viewing the obtained GTS as a complex structured MTS, a novel three-layer FCM architecture is devised. This FCM comprises a layer-3 FCM for extracting spatial relationships among parameters, a layer-2 FCM for extracting spatial relationships among variables, and a layer-1 FCM for capturing temporal relationships. By embedding the layer-3 FCM into the nodes of the layer-2 FCM and further embedding the layer-2 FCM into the nodes of the layer-1 FCM, the three-layer FCM can effectively capture and reflect temporal and spatial relationships while treating each complex element of the obtained GTS as a cohesive entity during forecasting. By constructing the three-layer FCM-based model at a granular level for MTS, the proposed approach mitigates accumulated errors and enhance the ability to forecast future trends with superior accuracy.

This paper investigates an improved dynamic guaranteed cost event-triggered-based anti-disturbance control for T-S fuzzy wind-turbine systems subject to external disturbances. A guaranteed cost event-triggered paradigm with dynamic threshold and sector structure is constructed to alleviate unnecessary triggers caused by outlier measurement. An additional event condition is designed to deal with the difference of premise variable between the system and controller. A PI-type intermediate estimator is introduced to simultaneously estimate the system state and external disturbance. Subsequently, an event-triggered fuzzy controller is built to actively compensate the external disturbances. With the help of Finsler's lemma, sufficient criteria are derived in terms of linear matrix inequalities to make the wind-turbine systems asymptotically stable. Finally, the proposed method is verified by comparative studies.

Accurate air quality forecasting is crucial for public health and addressing air pollution. However, the dynamic evolution trends, the cross-interference among different air quality indexes, and the error accumulation in the long-term prediction process are still open problems when establishing air quality forecasting models. Thus, we present a long-term interpretable air quality trend forecasting model to address these challenges via directed interval fuzzy cognitive maps, DE-DIFCM. Specifically, we design a time series trend extraction and representation learning module based on the interval fuzzy granules and the Cramer decomposition theorem in the first phase. Next, we formulate the interval information granules' time series forecasting as a directed interval fuzzy cognitive map (DIFCM). In particular, we employ PM2.5 as a benchmark to validate the performance of the proposed DE-DIFCM. Experimental results on six air quality monitoring datasets demonstrate the model's superior and competitive long-term prediction performance by comparison with some representative baselines.

Higher-order community detection reveals both mesoscale structures and functional characteristics of real-world networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher-order fuzzy community information. This study introduces a novel concept of higher-order fuzzy memberships that quantify the membership grades of motifs to crisp higher-order communities, thereby revealing partial community affiliations. Furthermore, we utilize higher-order fuzzy memberships to enhance higher-order community detection via a general framework called fuzzy memberships-assisted motif-based evolutionary modularity. On the one hand, a fuzzy membership-based neighbor community modification strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging strategy is proposed to combine excessively fragmented communities, enhancing local search ability. Experimental results indicate that the proposed framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in networks with ambiguous and complex structures.

China has implemented a series of ecological engineering projects to help achieve the 2060 carbon neutrality target. However, the lack of quantitative research on ecological engineering and the contribution of climate change to terrestrial carbon sinks limits this goal. This study uses robust statistical models combined with multiple terrestrial biosphere models to quantify the impact of China's ecological engineering on terrestrial ecosystem carbon sink trends and their differences according to the difference between reality and nonpractice assumptions. The main conclusions include the following: (1) since 1901, 84% of terrestrial ecosystem carbon sinks in China have shown an increasing trend, and approximately 45% of regional carbon sinks have increased by more than 0.1 g C/m2 every 10 years. (2) Considering the impact of human activities and the implementation of ecological engineering in China, approximately 56% of carbon sinks have improved, and approximately 10% of the regions whose carbon sink growth exceeds 50 g C m−2 yr−1 are mainly in the southeast coastal of China. (3) The carbon sequestration potential and effect of the Sanjiangyuan ecological protection and construction project are better than others, at 1.26 g C m−2 yr−1 and 14.13%, respectively. The Beijing–Tianjin sandstorm source comprehensive control project helps alleviate the reduction in carbon sinks, while the southwest karst rocky desertification comprehensive control project may aggravate the reduction in carbon sinks. This study clarifies the potential of China's different ecological engineering to increase carbon sink potential, and distinguishes and quantifies the contribution of climate and human activity factors to it, which is of great significance to the system management optimization scheme of terrestrial ecosystems and can effectively serve the national carbon neutral strategy.

The digital economy has emerged as an important force driving China’s economic development. However, little is known about its income distributive impact. This issue is even important in the context of China’s path to common prosperity, in which reducing income inequality is essential. This study combines the “Broadband China” strategy as a proxy variable for digital economic development with data from the China Family Panel Studies (CFPS) and identifies a highly significant impact of digital economy development on household income growth through a Difference-in-Differences (DID) setting. Digital economy particularly promotes the income growth among rural residents which means that the digital economy will contribute to the common prosperity. Further analysis shows that the underlying mechanisms through which the digital economy helps achieving the common prosperity involve the stimulation of agricultural production among rural residents, facilitation of non-agricultural employment opportunities for rural residents, and promotion of financial investments within rural households. These findings underscore the pivotal role of the digital economy in narrowing the income gap between urban and rural residents and fostering common prosperity.

Vanadium (V) is ubiquitously distributed in environmental media, imposing toxic hazards to organisms in biogeosphere. This review focuses on a comprehensive summary of the ecological and human health risks ascribed to V pollution, based on existing V toxicity relevant publications. Lower doses of V (< 2 mg/L) is beneficial to plant growth, however, V reduces plant biomass, induces toxic effects on plant morphology and adversely affects the absorption of essential plant elements at higher levels (≥ 2 mg/L). Oxidative stress induced by reactive oxygen species (ROS) produced under V stress may be the main reason for V toxicity to terrestrial and aquatic organisms. Although V contributes to high-efficient biological nitrogen fixation as a co-factor, V alters microbial community structures and drives its evolution. V can also accumulate in and harm human bodies. Knowledge gaps and further perspectives in aspects of V risks are thereby proposed, such as more toxic data reports for deriving V environmental quality criteria, elucidating the molecular mechanisms of detoxification and tolerance of organisms to V for bioremediation applications, and exploring the medical potential of V with the prevention of V toxicity to humans. This review advances our understanding of the toxic effects of V in the ecosystem and inspires future efforts on V management as well as application.

The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction. Significance Statement Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.

Baseflow is one of the major pathways of runoff in hilly areas, and its contributions to surface water resources and pollutant loads cannot be ignored. In this study, based on water quantity and quality data from 1988 to 2019 in hilly and low rainfall watersheds, we focused on the impact of long-term baseflow on nitrogen load using the load allocation based on the baseflow separation method. We also constructed a nitrogen balance model for the Chaohe River Basin of China from 2012 to 2021 to analyze the nitrogen accumulation in the basin. We used the baseflow nitrogen load lag analysis method to study the lag characteristics of the baseflow discharge process and analyzed the response and periodicity of baseflow nitrogen to precipitation and soil accumulation using time delay analysis. The results showed that the contribution rate of baseflow nitrogen reached 69% and showed a slight increasing trend from 1988 to 2019. The effects of changes in precipitation and nitrogen accumulation on the baseflow contribution was observed after 1–2 and 2 yr, respectively. After nitrogen accumulation, it entered the river channel through baseflow, which was already the main and continuous source of nitrogen in rivers in hilly areas.

Soil detachment plays a central role in the formulation of soil erosion models, particularly for the simulation and prediction of erosion in cold climates during the thaw period. This research attempts to elucidate the mechanisms of soil detachment on spring thaw period slopes through comprehensive flume experiments, coupled with the application of rare earth element (REE) tracer, investigating the relationships between soil detachment rates, sediment concentration and sediment transport capacity. Observations indicate a nuanced response of soil detachment rate and sediment concentration to scour duration, characterized by an initial increase, subsequent decrease and eventual equilibrium. As thaw depth increases, the primary source of eroded sediment gradually shifts from the upper slope to the mid‐slope. Soil detachment rate was affected by sediment concentration, flow discharge, slope gradient, thawing depth, and slope positions. Furthermore, our analysis reveals a power function relationship (R² = 0.846) between soil detachment rate, effective shear stress, and sediment transport rate and capacity. These results provide valuable insights into the modeling and prediction of soil erosion processes on brown soil slopes subjected to spring thaw period cycles.

In everyday conversation, bilingual individuals switch between their languages not only in reaction to monolinguals with different language profiles but also voluntarily and naturally. However, whether and how various switching contexts dynamically modulate domain-general cognitive control is still unclear. Using a cross-task paradigm in which a flanker task was interleaved with a language-switching task trial-by-trial, the present study examined the performance of unbalanced Chinese-English bilinguals on a flanker task in forced, voluntary, and natural switching contexts. The cross-domain interaction on the P3 component revealed an atypical flanker effect in forced switching contexts only, and the P3 amplitude of incongruent trials in forced switching contexts was smaller than in both natural and voluntary switching contexts. Furthermore, robust brain–brain and brain-behavior relationships between language control and domain-general control emerged in the forced switching context only. Altogether, our findings support the dynamic adaptation of language control to cognitive control and highlight the importance of different types of switching contexts.