Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition wi...Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.展开更多
Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. Ho...Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress ...Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress to more fairly reflect educational effectiveness.This evaluation method is particularly suitable for vocational education,effectively motivating students’learning enthusiasm and enhancing their self-confidence.Foreign research is represented by the Tennessee Value-Added Assessment System(TVAAS),widely used in evaluating school quality and teacher performance.Domestic research currently focuses on the theoretical construction,model establishment,optimization,and practical application of value-added evaluation,still facing significant challenges in data collection comprehensiveness and model adaptability.Aiming at current issues,this study focuses on exploring the application of artificial intelligence large models in student value-added evaluation from an evidence-based perspective,committed to constructing an innovative evidence-based value-added evaluation system.It aims to achieve precise assessment of students’learning effect“net value-added”through multi-source data collection,intelligent analysis,and personalized feedback.The system integrates outcome evaluation,process evaluation,value-added evaluation,and comprehensive evaluation to form a“four-in-one”dynamic evaluation framework,considering students’starting points,process performance,and final achievements.In the future,value-added evaluation needs to further expand the assessment of non-academic dimensions(such as professional literacy and social-emotional skills)and explore the application of non-linear models to promote the deepening and innovation of educational evaluation reform.展开更多
Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of ...Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of its value-added products and their profitability in the North-West Region remains underexplored. This study examined the profitability of maize value-added products in Mezam Division, with the objectives to: 1) identify various maize-based products, 2) assess the diversity of these products, 3) conduct a cost-benefit analysis of selected products, 4) examine the relationship between profitability and product diversity, and 5) identify key constraints impacting profitability. To achieve these objectives, structured questionnaires were administered to 500 small-scale maize entrepreneurs randomly selected from five subdivisions. Descriptive statistics were used to analyze objective 1 and 5, while the Shannon Diversity Index was employed to assess product diversity. Additionally, a cost-benefit analysis was conducted on four selected products namely pap, parched corn, peeled parboiled corn, and corn beer, and a correlation analysis was used to examine objective 4. In total, 13 maize value-added products were identified, with a diversity index of 4.4. The total cost of processing the four selected products per entrepreneur using 18 kg of maize per product was FCFA 83631.5 (US $132.75), while the total revenue was FCFA 121864.5 (US $193.43), resulting in an economic profit of FCFA 38,233 (US $60.69). Pap emerged as the most profitable product, with an economic profit of FCFA 27,875 (US $44.24), while corn beer was the least profitable, with an economic profit of FCFA 2133.46 (US $3.39). The correlation analysis revealed a strong negative relationship between product diversity and profitability (r = −0.91), indicating that entrepreneurs can maximize profitability by focusing on a few high-demand products like pap and parched corn. Key constraints to profitability included fluctuating market prices, high production costs, limited access to finance, and inadequate storage facilities. Despite these challenges, our findings indicate that maize value addition is profitable in Mezam Division. Entrepreneurs can leverage this data for informed decision-making and future investments. It is recommended that the government promote maize value addition and provide financial support for modern processing equipment to boost profitability and income generation.展开更多
Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while signific...Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.展开更多
In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to ...In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.展开更多
In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrabilit...In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific im...In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific implementation of rural revitalization strategy.Based on the entry of rural collectively-owned construction land into the market and the compensation system for land expropriation,this paper discusses in detail the distribution of value-added income of rural land collective ownership,analyzes the current situation,existing problems and causes of the current distribution mechanism,and puts forward countermeasures and suggestions for optimizing the distribution mechanism.Through literature research and case analysis,this paper reveals the unfair phenomenon in the distribution of value-added income of rural land,and discusses the roles and responsibilities of government,collective organizations and individual farmers in the distribution of income.The results show that establishing a fair and reasonable income distribution mechanism,strengthening the construction of laws and regulations,improving farmers participation and protecting their rights and interests are the key to optimizing the distribution of rural land value-added income.In addition,it is expected that this paper will provide some theoretical basis and practical guidance for improving the distribution mechanism of value-added income of rural land collective ownership.展开更多
International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ...International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ascent,using an undirected weighted network of joint patent applications and firm-level data.By analyzing the network’s structural characteristics and its evolution,we explore the mechanisms through which collaboration drives the rise of manufacturing enterprises within global value chains.Our findings show that:(1)China’s rapidly expanding collaborative innovation network features a distinct“core-periphery”structure,with leading firms,universities,and government research institutions at its center.(2)By strengthening market power and enabling firms to take on more advanced production,collaborative innovation contributes to a higher domestic value-added rate in exports.(3)Heterogeneity analysis reveals that the impact of collaborative innovation on moving up the value chain is particularly evident for firms with strong production and technology absorption capabilities,those positioned lower in the value chain,and those facing fewer trade barriers.展开更多
We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various re...We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.展开更多
As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional t...As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.展开更多
Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale opti...Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.展开更多
Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special...Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.展开更多
When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and ne...When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.展开更多
The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of...The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.展开更多
High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase ...High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase in matrix was found to have significant impact on the properties of HEAs.Nevertheless,the morphology evolution and the kinetics of SD is not clear,which hinders in-depth understanding of the structure-property relationship.In this study,we examine the spinodal structures in(FeCoCrNi)85(AlCu)15 HEAs at different states using in-situ small-angle neutron scattering(SANS),in conjunction with transmission electron microscopy technique.The result demonstrates that SD occurred when aging the HEA samples at temperatures ranging from 500 to 800℃,which leads to the phase constitution of NiAlCu-rich and FeCoCr-rich spinodal phases,L1_(2)ordered phases,and FCC matrix.The characteristic wavelength of SD(λ_(SD))grows from 5.31 to 51.26 nm when aging temperature rises from 500 to 800℃,which explains the enhancement of the alloy’s microhardness.The SD kinetics was unraveled by fitting the time-dependentλ_(SD)through in-situ SANS measurement at 700℃.During isothermal treatment at 700℃,theλ_(SD)increases from 10.42 to 17.43 nm with prolonged time,and SD is in the late stage from the exponential trend of theλ_(SD)over time.Moreover,comparing with aging temperature,the aging time has a relatively minor impact on the coarsening of SD.展开更多
Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving fac...Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.展开更多
基金supported by Ningbo Natural Science Foundation(No.2023J059)Ningbo Commonweal Programme Key Project(No.2023S038)Guangxi Key Research and Development Programme(No.GuikeAB21220063).
文摘Machine-learning is a robust technique for understanding pollution characteristics of surface ozone,which are at high levels in urban China.This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to investigate ozone dynamics and formation regimes in a coastal area of China.During the period of 2017–2022,significant inter-annual fluctuations emerged,with peaks in mid-2017 attributed to volatile organic compounds(VOCs),and in late-2019 influenced by air temperature.Multifaceted periodicities(daily,weekly,holiday,and yearly)in ozone were revealed,elucidating substantial influences of daily and yearly components on ozone periodicity.A VOC-sensitive ozone formation regime was identified,characterized by lower VOCs/NO_(x) ratios(average=0.88)and significant positive correlations between ozone and VOCs.This interplay manifested in elevated ozone duringweekends,holidays,and pandemic lockdowns.Key variables influencing ozone across diverse timescaleswere uncovered,with solar radiation and temperature driving daily and yearly ozone variations,respectively.Precursor substances,particularly VOCs,significantly shaped weekly/holiday patterns and long-term trends of ozone.Specifically,acetone,ethane,hexanal,and toluene had a notable impact on the multi-year ozone trend,emphasizing the urgency of VOC regulation.Furthermore,our observations indicated that NO_(x) primarily drived the stochastic variations in ozone,a distinguishing characteristic of regions with heavy traffic.This research provides novel insights into ozone dynamics in coastal urban areas and highlights the importance of integrating statistical and machinelearning methods in atmospheric pollution studies,with implications for targeted mitigation strategies beyond this specific region and pollutant.
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金supported by the National Natural Science Foundation of China (Nos. 22379121, 62005216)Basic Public Welfare Research Program of Zhejiang (No. LQ22F050013)+1 种基金Zhejiang Province Key Laboratory of Flexible Electronics Open Fund (2023FE005)Shenzhen Foundation Research Program (No. JCYJ20220530112812028)。
文摘Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金Artificial Intelligence Education Research Project of Shandong Provincial Audio-Visual Education Center“Exploration of the Application of Large-scale AI Models in Student Value-added Evaluation from an Evidence-based Perspective”(SDDJ202501035)。
文摘Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress to more fairly reflect educational effectiveness.This evaluation method is particularly suitable for vocational education,effectively motivating students’learning enthusiasm and enhancing their self-confidence.Foreign research is represented by the Tennessee Value-Added Assessment System(TVAAS),widely used in evaluating school quality and teacher performance.Domestic research currently focuses on the theoretical construction,model establishment,optimization,and practical application of value-added evaluation,still facing significant challenges in data collection comprehensiveness and model adaptability.Aiming at current issues,this study focuses on exploring the application of artificial intelligence large models in student value-added evaluation from an evidence-based perspective,committed to constructing an innovative evidence-based value-added evaluation system.It aims to achieve precise assessment of students’learning effect“net value-added”through multi-source data collection,intelligent analysis,and personalized feedback.The system integrates outcome evaluation,process evaluation,value-added evaluation,and comprehensive evaluation to form a“four-in-one”dynamic evaluation framework,considering students’starting points,process performance,and final achievements.In the future,value-added evaluation needs to further expand the assessment of non-academic dimensions(such as professional literacy and social-emotional skills)and explore the application of non-linear models to promote the deepening and innovation of educational evaluation reform.
文摘Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of its value-added products and their profitability in the North-West Region remains underexplored. This study examined the profitability of maize value-added products in Mezam Division, with the objectives to: 1) identify various maize-based products, 2) assess the diversity of these products, 3) conduct a cost-benefit analysis of selected products, 4) examine the relationship between profitability and product diversity, and 5) identify key constraints impacting profitability. To achieve these objectives, structured questionnaires were administered to 500 small-scale maize entrepreneurs randomly selected from five subdivisions. Descriptive statistics were used to analyze objective 1 and 5, while the Shannon Diversity Index was employed to assess product diversity. Additionally, a cost-benefit analysis was conducted on four selected products namely pap, parched corn, peeled parboiled corn, and corn beer, and a correlation analysis was used to examine objective 4. In total, 13 maize value-added products were identified, with a diversity index of 4.4. The total cost of processing the four selected products per entrepreneur using 18 kg of maize per product was FCFA 83631.5 (US $132.75), while the total revenue was FCFA 121864.5 (US $193.43), resulting in an economic profit of FCFA 38,233 (US $60.69). Pap emerged as the most profitable product, with an economic profit of FCFA 27,875 (US $44.24), while corn beer was the least profitable, with an economic profit of FCFA 2133.46 (US $3.39). The correlation analysis revealed a strong negative relationship between product diversity and profitability (r = −0.91), indicating that entrepreneurs can maximize profitability by focusing on a few high-demand products like pap and parched corn. Key constraints to profitability included fluctuating market prices, high production costs, limited access to finance, and inadequate storage facilities. Despite these challenges, our findings indicate that maize value addition is profitable in Mezam Division. Entrepreneurs can leverage this data for informed decision-making and future investments. It is recommended that the government promote maize value addition and provide financial support for modern processing equipment to boost profitability and income generation.
文摘Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.
基金supported by the National Natural Science Foundation of China(Nos.52470114 and 52022104)the National Key R&D Program of China(No.2022YFC3702802)the Youth Innovation Promotion Association,CAS(No.Y2021020).
文摘In recent years,ozone has become one of the key pollutants affecting the urban air qual-ity.Direct catalytic decomposition of ozone emerges as an effective method for ozone re-moval.Field experimentswere conducted to evaluate the effectiveness of exteriorwall coat-ings with ozone decomposition catalysts for ozone removal in practical applications.ANSYS 2020R1 software was first used for simulation and analysis of ozone concentration and flow fields to investigate the decomposition boundary of these wall coatings.The results show that the exterior wall coatings with manganese-based catalysts can effectively reduce the ozone concentration near the wall coating.The ozone decomposition efficiency is nega-tively correlated with the distance fromthe coating and the decomposition boundary range is around 18 m.The decomposition boundary will increase with the increase of tempera-ture,and decrease with the increase of the wind speed and the relative humidity.These results underscore the viability of using exterior wall coatings with catalysts for controlling ozone pollution in atmospheric environments.This approach presents a promising avenue for addressing ozone pollution through self-purifying materials on building external wall.
基金sponsored by the National Natural Science Foundations of China under Grant Nos.12301315,12235007,11975131the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ20A010009。
文摘In the realm of nonlinear integrable systems,the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability.By focusing on single-component decompositions within the potential BKP hierarchy,it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations.Moreover,through the implementation of multi-component decompositions within the potential BKP hierarchy,successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled Kd V-type systems that resist decoupling via alterations in dependent variables.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
文摘In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific implementation of rural revitalization strategy.Based on the entry of rural collectively-owned construction land into the market and the compensation system for land expropriation,this paper discusses in detail the distribution of value-added income of rural land collective ownership,analyzes the current situation,existing problems and causes of the current distribution mechanism,and puts forward countermeasures and suggestions for optimizing the distribution mechanism.Through literature research and case analysis,this paper reveals the unfair phenomenon in the distribution of value-added income of rural land,and discusses the roles and responsibilities of government,collective organizations and individual farmers in the distribution of income.The results show that establishing a fair and reasonable income distribution mechanism,strengthening the construction of laws and regulations,improving farmers participation and protecting their rights and interests are the key to optimizing the distribution of rural land value-added income.In addition,it is expected that this paper will provide some theoretical basis and practical guidance for improving the distribution mechanism of value-added income of rural land collective ownership.
基金supported by the National Social Science Fund of China(NSSFC)“Research on Collaborative Innovation and Global Value Chain Upgrading in Manufacturing”(Grant No.23CJL019)“Research on the Advantages of Ultra-Large-Scale Market and the Construction of Modern Industrial System”(Grant No.23&ZD041).
文摘International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ascent,using an undirected weighted network of joint patent applications and firm-level data.By analyzing the network’s structural characteristics and its evolution,we explore the mechanisms through which collaboration drives the rise of manufacturing enterprises within global value chains.Our findings show that:(1)China’s rapidly expanding collaborative innovation network features a distinct“core-periphery”structure,with leading firms,universities,and government research institutions at its center.(2)By strengthening market power and enabling firms to take on more advanced production,collaborative innovation contributes to a higher domestic value-added rate in exports.(3)Heterogeneity analysis reveals that the impact of collaborative innovation on moving up the value chain is particularly evident for firms with strong production and technology absorption capabilities,those positioned lower in the value chain,and those facing fewer trade barriers.
文摘We presented the preparation and analysis of La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts,supported on microwave-absorbing ceramic carriers,using the sol-gel method.We systematically investigated the effects of various reaction conditions under microwave irradiation(0-50 W).These conditions included reaction temperatures(300-600℃),oxygen concentrations(0-6%),and varying K^(+)doping levels on the catalysts'activity.The crystalline phase,microstructure,and the catalytic activity of the catalyst were analyzed by XRD,TEM,H_2-TPR,and O_(2)-TPD.The experimental results reveal that La_(1-x)K_(x)CoO_(3)(x=0.1-0.4)catalysts consistently form homogeneous perovskite nanoparticles across different doping levels.The NO decomposition efficiency on these catalysts initially increases and then decreases with variations in doping amount,temperature,and microwave power.Additionally,an increase in oxygen concentration positively influences NO conversion rates.The optimal performance is observed with La_(0.7)K_(0.3)CoO_(3)catalyst under conditions of x=0.3,400℃,10 W microwave power,and 4%oxygen concentration,achieving a peak NO conversion rate of La_(0.7)K_(0.3)CoO_(3)catalyst is 93.1%.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974154 and 12304278)the Taishan Scholars Special Funding for Construction Projects(Grant No.tstp20230622)+1 种基金the Natural Science Foundation of Shandong Province(Grant Nos.ZR2022MA004,ZR2023QA127,and ZR2024QA121)the Special Foundation of Yantai for Leading Talents above Provincial Level.
文摘As an independent thermodynamic parameter,pressure significantly influences interatomic distances,leading to an increase in material density.In this work,we employ the CALYPSO structure search and density functional theory calculations to explore the structural phase transitions and electronic properties of calcium-sulfur compounds(Ca_(x)S_(1-x),where x=1/4,1/3,1/2,2/3,3/4,4/5)under 0-1200 GPa.The calculated formation enthalpies suggest that Ca_(x)S_(1-x)compounds undergo multiple phase transitions and eventually decompose into elemental Ca and S,challenging the traditional view that pressure stabilizes and densifies compounds.The analysis of formation enthalpy indicates that an increase in pressure leads to a rise in internal energy and the PV term,resulting in thermodynamic instability.Bader charge analysis reveals that this phenomenon is attributed to a decrease in charge transfer under high pressure.The activation of Ca-3d orbitals is significantly enhanced under pressure,leading to competition with Ca-4s orbitals and S-3p orbitals.This may cause the formation enthalpy minimum on the convex hull to shift sequentially from CaS to CaS_(3),then to Ca_(3)S and Ca_(2)S,and finally back to CaS.These findings provide critical insights into the behavior of alkaline-earth metal sulfides under high pressure,with implications for the synthesis and application of novel materials under extreme conditions and for understanding element distribution in planetary interiors.
基金The Australian Research Council(DP200101197,DP230101107).
文摘Formalizing complex processes and phenomena of a real-world problem may require a large number of variables and constraints,resulting in what is termed a large-scale optimization problem.Nowadays,such large-scale optimization problems are solved using computing machines,leading to an enormous computational time being required,which may delay deriving timely solutions.Decomposition methods,which partition a large-scale optimization problem into lower-dimensional subproblems,represent a key approach to addressing time-efficiency issues.There has been significant progress in both applied mathematics and emerging artificial intelligence approaches on this front.This work aims at providing an overview of the decomposition methods from both the mathematics and computer science points of view.We also remark on the state-of-the-art developments and recent applications of the decomposition methods,and discuss the future research and development perspectives.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFB2903904。
文摘Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the Guangxi Key Research and Development Projects of China(GuikeAB21238004)the Agricultural Science and Technology Innovation Program.
文摘When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.
文摘The deployment of non-precious metal catalysts for the production of COx-free hydrogen via the ammonia decomposition reaction(ADR)presents a promising yet great challenge.In the present study,two crystal structures of α-MoC and β-Mo_(2)C catalysts with different Mo/C ratios were synthesized,and their ammonia decomposition performance as well as structural evolution in ADR was investigated.The β-Mo_(2)C catalyst,characterized by a higher Mo/C ratio,demonstrated a remarkable turnover frequency of 1.3 s^(-1),which is over tenfold higher than that ofα-MoC(0.1 s^(-1)).An increase in the Mo/C ratio of molybdenum carbide revealed a direct correlation between the surface Mo/C ratio and the hydrogen yield.The transient response surface reaction indicated that the combination of N*and N*derived from NH_(3) dissociation represents the rate-determining step in the ADR,andβ-Mo2C exhibited exceptional proficiency in facilitating this pivotal step.Concurrently,the accumulation of N*species on the carbide surface could induce the phase transition of molybdenum carbide to nitride,which follows a topological transformation.It is discovered that such phase evolution was affected by the Mo-C surface and reaction temperature simultaneously.When the kinetics of combination of N*was accelerated by rising temperatures and its accumulation on the carbide surface was mitigated,β-Mo_(2)C maintained its carbide phase,preventing nitridation during the ADR at 810℃.Our results contribute to an in-depth understanding of the molybdenum carbides’catalytic properties in ADR and highlight the nature of the carbide-nitride phase transition in the reaction.
基金financially supported by the National Key Research and Development Program of China(Grant No.2021YFA1600701)the Guangdong Basic and Applied Basic Research Foundation,China(Project No.2021B1515140028)the National Natural Science Foundation of China(Grant No.12275154)。
文摘High entropy alloys(HEAs)constituted of single solid solution phase,but remains chemical inhomogeneity in nature due to its multi-principal composition.Currently,existence of nanoscale spinodal decomposition(SD)phase in matrix was found to have significant impact on the properties of HEAs.Nevertheless,the morphology evolution and the kinetics of SD is not clear,which hinders in-depth understanding of the structure-property relationship.In this study,we examine the spinodal structures in(FeCoCrNi)85(AlCu)15 HEAs at different states using in-situ small-angle neutron scattering(SANS),in conjunction with transmission electron microscopy technique.The result demonstrates that SD occurred when aging the HEA samples at temperatures ranging from 500 to 800℃,which leads to the phase constitution of NiAlCu-rich and FeCoCr-rich spinodal phases,L1_(2)ordered phases,and FCC matrix.The characteristic wavelength of SD(λ_(SD))grows from 5.31 to 51.26 nm when aging temperature rises from 500 to 800℃,which explains the enhancement of the alloy’s microhardness.The SD kinetics was unraveled by fitting the time-dependentλ_(SD)through in-situ SANS measurement at 700℃.During isothermal treatment at 700℃,theλ_(SD)increases from 10.42 to 17.43 nm with prolonged time,and SD is in the late stage from the exponential trend of theλ_(SD)over time.Moreover,comparing with aging temperature,the aging time has a relatively minor impact on the coarsening of SD.
基金the National Natural Science Foundation of China[Grant No.52270183].
文摘Exploring the factors driving the decoupling of China’s sulfur dioxide(SO_(2))emissions from economic growth(DEI)is crucial for achieving sustainable development.By analyzing the decoupling indicators and driving factors at both the generation and treatment stages of SO_(2),more effective targeted mitigation strategies can be developed.We employ the Tapio decoupling model and propose a two-stage method to examine the decoupling issues related to SO_(2).Our findings indicate that:①DEI shows a steady and significant improvement,with SO_(2)emission intensity identified as the primary driver.②for the decoupling of economic growth and SO_(2)generation,energy scale serves as the largest stimulator,while the effect of energy intensity changes from negative to positive,and pollution intensity is first positive and then negative.③For the decoupling of SO_(2)generation and SO_(2)removal,treatment efficiency leads as the largest promoter,followed by treatment intensity.Based on these results,this study recommends that China focuses more on enhancing clean energy utilization and the effectiveness of treatment processes.
