Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and car...This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.展开更多
Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and ac...Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and accuracy in weapon system strikes.Piezoelectric actuators(PEAs),known for their nanometer-level precision,flexible stroke,resistance to electromagnetic interference,and scalable structure,have been widely adopted across various fields.Therefore,this study focuses on extreme scenarios involving ultra-high precision(micrometer and beyond),minuscule scales,and highly complex operational conditions.It provides a comprehensive overview of the types,working principles,advantages,and disadvantages of PEAs,along with their potential applications in piezo-actuated smart mechatronic systems(PSMSs).To address the demands of extreme scenarios in high-end equipment fields,we have identified five representative application areas:positioning and alignment,biomedical device configuration,advanced manufacturing and processing,vibration mitigation,micro robot system.Each area is further divided into specific subcategories,where we explore the underlying relationships,mechanisms,representative schemes,and characteristics.Finally,we discuss the challenges and future development trends related to PEAs and PSMSs.This work aims to showcase the latest advancements in the application of PEAs and provide valuable guidance for researchers in this field.展开更多
With the rapid development of virtual reality(VR)and augmented reality(AR)technologies,their application potential in the field of education has become increasingly significant.For a long time,fire safety education in...With the rapid development of virtual reality(VR)and augmented reality(AR)technologies,their application potential in the field of education has become increasingly significant.For a long time,fire safety education in university laboratories has faced numerous challenges,and traditional teaching methods have been insufficiently effective,with high-risk scenarios difficult to realistically recreate.Especially in special scenarios involving hazardous chemicals,conventional training methods struggle to enable learners to achieve deep understanding and behavioral formation.This study systematically integrates immersive technology theory with safety education needs,providing a replicable technical solution for safety education in high-risk environments.Its modular design approach has reference value for expansion into other professional fields,offering practical evidence for innovation in safety education models in the digital age.展开更多
The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid ...The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid growth in production and consumption.To formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related fields.Multidimensional technical parameters are selected and predicted accurately and reliably in combination with different development scenarios.The findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development scenarios.Under this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in 2060.Thetransportation sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the future.Additionally,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.展开更多
A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These...A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.展开更多
Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data ...Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.展开更多
Climate change and anthropogenic pressures increasingly threaten the ecological integrity of inland water bodies,particularly saline lakes due to their unique hydrological and biological features.This review focuses o...Climate change and anthropogenic pressures increasingly threaten the ecological integrity of inland water bodies,particularly saline lakes due to their unique hydrological and biological features.This review focuses on Lake Tudakul,one of Uzbekistan’s largest saline lakes and a Ramsar-listed wetland,assessing its vulnerability under future climate scenarios.The study integrates climate scenario modeling(RCP4.5 and RCP8.5)with standardized ecotoxicological bioassays—Microtox®,MARA,algal growth inhibition,Lemna minor,and Daphnia magna toxicity tests—to evaluate combined effects of rising temperatures(2.0℃and 4.5℃)and chemical pollutants.Results reveal increased biological sensitivity to contaminants under elevated temperatures,suggesting potential synergistic impacts that may disrupt lake ecosystem structure and function.Lake Tudakul,a regional biodiversity hotspot,is exposed to agrochemical runoff,increasing salinity,and microplastic pollution,threatening aquatic organisms and ecological services.The accumulation and trophic transfer of pollutants—such as heavy metals,persistent organic compounds,and micro(nano)plastics—pose risks to food webs,public health,and water safety.These stressors may also increase the likelihood of harmful algal blooms and cyanotoxin outbreaks.The study emphasizes the urgent need for early-warning systems,adaptive management,and transboundary cooperation to mitigate ecological risks.Lake Tudakul exemplifies the vulnerability of semi-arid lakes under compounding climate and human pressures,highlighting the importance of integrative,ecosystem-based strategies to safeguard biodiversity and freshwater resources.展开更多
With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is ...With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent,and the accuracy of typical scenario predictions is low.In order to improve the accuracy of scenario prediction under source and load uncertainty,this paper proposes a typical scenario identification model based on random forests and order parameters.Firstly,a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids,taking into account source-load uncertainty.Secondly,the dynamic change characteristics of the order parameters of the daily load curve,wind and solar curve,and load curve of typical scenarios are statistically analyzed to identify the key order parameters that have the most significant impact on the uncertainty of the load.Then,the order parameters and seasonal distribution are used as features to train a random forest classification model to achieve efficient scenario prediction.Finally,the simulation of actual data from a provincial distribution network shows that the proposed method can accurately classify typical scenarios with an accuracy rate of 92.7%.Additionally,sensitivity analysis is conducted to assess how changes in uncertainty levels affect the importance of each order parameter,allowing for adaptive uncertainty mitigation strategies.展开更多
The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and manag...The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and management,yet the lack of user-friendly tools for model development remains a major bottleneck.This study presents the Multi-Scenario Crop Disease Forecasting Modeling System(MSDFS),an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training,evaluation,and deployment-across four representative scenarios:static point-based,static grid-based,dynamic point-based,and dynamic grid-based.Unlike conventional frameworks,MSDFS emphasizes modeling flexibility,allowing users to build,compare,and interpret diverse forecasting approaches within a unified workflow.A notable feature of the system is the integration of a weather scenario generator,which facilitates comprehensive testing of model performance and adaptability under extreme climatic conditions.Case studies corresponding to the four scenarios were used to validate the system,with overall accuracy(OA)ranging from 73%to 93%.By lowering technical barriers,the system is designed to serve plant protection managers and agricultural producers without advanced programming expertise,providing a practical modeling tool that supports the construction of smart plant protection systems.展开更多
Objective:To investigate the effectiveness of standardized patient scenario simulation teaching in geriatric medicine clinical education and provide references for improving teaching methods in geriatrics.Methods:Sixt...Objective:To investigate the effectiveness of standardized patient scenario simulation teaching in geriatric medicine clinical education and provide references for improving teaching methods in geriatrics.Methods:Sixty-five clinical physicians from other departments who rotated into the Geriatric Medicine Department for training between August 2024 and July 2025 were randomly divided into a control group(n=32)and an observation group(n=33).The control group received traditional centralized theoretical lectures combined with instructor-led clinical mentoring,while the observation group underwent standardized patient scenario simulation training.The two groups were compared on post-rotation examination scores and teaching satisfaction metrics.Results:The observation group achieved significantly higher post-rotation examination scores(88.37±3.04)than the control group(80.17±3.29)(p<0.01).Teaching satisfaction surveys revealed that trainees in the observation group demonstrated significantly higher satisfaction than the control group(p<0.05)regarding the teaching method’s effectiveness in enhancing learning interest,independent learning ability,comprehensive clinical problem-solving skills,patient communication skills,teamwork capabilities,and research conceptualization abilities.Conclusion:Standardized patient scenario simulation teaching effectively improves clinical teaching quality in geriatric medicine,enhances trainees’comprehensive clinical competencies,and holds value for broader application.展开更多
With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food tra...With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food trade and security.This study aims to identify the supply-demand match between cropland supply and food consumption and to evaluate sustainable cropland zoning in multiple scenarios and multidimensional assessments.This study uses ecological,environmental and socioeconomic data to quantify diverse food demand patterns into corresponding cropland demands,further mapping the spatio-temporal characteristics of China's cropland supply-demand matches.By utilizing shared socioeconomic pathways(SSPs),this study delineates multiple scenarios to determine the supply-demand of cropland across different Chinese regions from 2030 to 2050.On the basis of ecological,geographical and socioeconomic datasets,this study constructs a multidimensional and multiscenario framework for sustainable agricultural zoning from 2030 to 2050 and proposes a future sustainable agricultural development strategy for each region in different periods.The results indicate that between 2002 and 2022,there was a significant gap between cropland supply and demand.Moreover,an obvious spatial mismatch is observed between cropland supply and demand across various Chinese regions.From 2030 to 2050,there is a noticeable shift in the spatial distribution of cropland supply and demand,with the supply-demand match becoming more strained and varying considerably under different development scenarios.With significant differences between different development scenarios,different regions will have to adopt different development strategies at different periods.This study proposes a multiscenario and multidimensional simulation framework for future agricultural sustainable zoning,which aims to provide scientific insights and policy improvements to promote sustainable agricultural development.展开更多
Flood regulation service(FRS)stands as one of the key benefits that people get from the ecosystem.Under the influence of climate change and human activities,the relationship between supply and demand of FRS would incr...Flood regulation service(FRS)stands as one of the key benefits that people get from the ecosystem.Under the influence of climate change and human activities,the relationship between supply and demand of FRS would increasingly affect regional flood risk and sustainable development.However,there was currently a lack of systematic study on the future supply-demand relationship of FRS in the flood-vulnerable area undergoing rapidly development in China.This study integrated the Scenario Model Intercomparison Project(ScenarioMIP)with the Shared Socioeconomic Pathways(SSPs)datasets and climate model data to quantify the supply-demand ratio(SDR)of FRS in the Yangtze River Delta(YRD),China from 2020 to 2050.Trend analyses were conducted using linear regres-sion,Theil-Sen median estimation,and Hurst exponent analysis,while key drivers of SDR changes were identified and quantified through the Lindeman-Merenda-Gold(LMG)method between 2021 and 2050.Results show that the supply of FRS in the YRD was generally insufficient to meet the demand.The imbalanced subbasins covered 88.24%of the total study area,with 34.48%of this imbal-anced area concentrated in the Southeastern Basin in China.During 2021 and 2050,the imbalance of FRS supply-demand relationship would largely aggravate in the YRD,of which the aggravated area would account for 77.23%.Under different scenarios,the SDR for FRS would decrease significantly,with rates ranging from-5.45×10^(-4) to-2.06×10^(-4)(P<0.05).Especially,the decline rate of SDR in the YRD Basin(DeltaB)reached 2.92 times that the average of YRD.Human activities were the primary factors that exacerbated the imbalance in FRS supply-demand relationship,of which the relative contribution rate exceeds 75%.Particular attention should be direc-ted toward critical regions like the Southeast Basin in China(SEB)and DeltaB where substantial aggravation of supply-demand imbal-ances of FRS is projected.展开更多
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr...Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.展开更多
Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),i...Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.展开更多
Emergency resources play a vital role in the emergency rescue process.The adequate and timely supply of emergency resources can effectively control the development of accidents and reduce accident losses.However,the c...Emergency resources play a vital role in the emergency rescue process.The adequate and timely supply of emergency resources can effectively control the development of accidents and reduce accident losses.However,the current emergency resource allocation of chemical enterprises lacks scientific analysis of accident scenarios,and the individual allocation method of enterprises increases the cost of emergency resource allocation.Given the above problems,this paper proposes a regional collaborative allocation method of emergency resources for enterprises within the chemical industry park(CIP)based on the worst credible accident scenario(WCAS).Firstly,the concept and analysis method of the WCAS is proposed.Then,based on the characteristics and consequences of the accident,the mapping relationship between accident scenarios and emergency resources is established.Finally,an optimization model for regional collaborative allocation of emergency resources is constructed to determine the amount of emergency resource allocation for each enterprise.Through the case study,the emergency resource allocation method based on the WCAS analysis can better meet the demands of accident emergency rescue.Simultaneously,the regional collaborative allocation optimization model can strengthen the cooperation ability among enterprises,greatly reducing the cost of emergency resource allocation for each enterprise.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development...The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.展开更多
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金supported by the top-level design of the National Natural Science Foundation of China(NSFC)Major Project“Realization of optimal carbon neutral pathway and coupling of multi-scale interaction patterns of natural-social systems in China”(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFC2204203)the National Natural Science Foundation of China(Grant No.52305107)。
文摘Precision actuation is a foundational technology in high-end equipment domains,where stroke,velocity,and accuracy are critical for processing and/or detection quality,precision in spacecraft flight trajectories,and accuracy in weapon system strikes.Piezoelectric actuators(PEAs),known for their nanometer-level precision,flexible stroke,resistance to electromagnetic interference,and scalable structure,have been widely adopted across various fields.Therefore,this study focuses on extreme scenarios involving ultra-high precision(micrometer and beyond),minuscule scales,and highly complex operational conditions.It provides a comprehensive overview of the types,working principles,advantages,and disadvantages of PEAs,along with their potential applications in piezo-actuated smart mechatronic systems(PSMSs).To address the demands of extreme scenarios in high-end equipment fields,we have identified five representative application areas:positioning and alignment,biomedical device configuration,advanced manufacturing and processing,vibration mitigation,micro robot system.Each area is further divided into specific subcategories,where we explore the underlying relationships,mechanisms,representative schemes,and characteristics.Finally,we discuss the challenges and future development trends related to PEAs and PSMSs.This work aims to showcase the latest advancements in the application of PEAs and provide valuable guidance for researchers in this field.
文摘With the rapid development of virtual reality(VR)and augmented reality(AR)technologies,their application potential in the field of education has become increasingly significant.For a long time,fire safety education in university laboratories has faced numerous challenges,and traditional teaching methods have been insufficiently effective,with high-risk scenarios difficult to realistically recreate.Especially in special scenarios involving hazardous chemicals,conventional training methods struggle to enable learners to achieve deep understanding and behavioral formation.This study systematically integrates immersive technology theory with safety education needs,providing a replicable technical solution for safety education in high-risk environments.Its modular design approach has reference value for expansion into other professional fields,offering practical evidence for innovation in safety education models in the digital age.
基金supported by the National Natural Science Foundation of China(No.71704178)Beijing Municipal Excellent Talents Foundation(No.2017000020124G133)Major consulting project of the Chinese Academy of Engineering(Nos.2023-JB-08,2022-PP-03).
文摘The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in China.With a wide range of application scenarios,hydrogen energy will experience rapid growth in production and consumption.To formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related fields.Multidimensional technical parameters are selected and predicted accurately and reliably in combination with different development scenarios.The findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development scenarios.Under this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in 2060.Thetransportation sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the future.Additionally,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.
文摘A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.
基金supported by the National Natural Science Foundation of China(Grant No.72374081)the Key Research and Development Project of the Department of Science and Technology of Jilin Province(Grant No.20240304164SF).
文摘Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.
基金supported by Türkiye Council of Higher Education Research Universities Support Program(Project Number:32762).
文摘Climate change and anthropogenic pressures increasingly threaten the ecological integrity of inland water bodies,particularly saline lakes due to their unique hydrological and biological features.This review focuses on Lake Tudakul,one of Uzbekistan’s largest saline lakes and a Ramsar-listed wetland,assessing its vulnerability under future climate scenarios.The study integrates climate scenario modeling(RCP4.5 and RCP8.5)with standardized ecotoxicological bioassays—Microtox®,MARA,algal growth inhibition,Lemna minor,and Daphnia magna toxicity tests—to evaluate combined effects of rising temperatures(2.0℃and 4.5℃)and chemical pollutants.Results reveal increased biological sensitivity to contaminants under elevated temperatures,suggesting potential synergistic impacts that may disrupt lake ecosystem structure and function.Lake Tudakul,a regional biodiversity hotspot,is exposed to agrochemical runoff,increasing salinity,and microplastic pollution,threatening aquatic organisms and ecological services.The accumulation and trophic transfer of pollutants—such as heavy metals,persistent organic compounds,and micro(nano)plastics—pose risks to food webs,public health,and water safety.These stressors may also increase the likelihood of harmful algal blooms and cyanotoxin outbreaks.The study emphasizes the urgent need for early-warning systems,adaptive management,and transboundary cooperation to mitigate ecological risks.Lake Tudakul exemplifies the vulnerability of semi-arid lakes under compounding climate and human pressures,highlighting the importance of integrative,ecosystem-based strategies to safeguard biodiversity and freshwater resources.
基金supported by Science and Technology Project Managed by the State Grid Jiangsu Electric Power Co.,Ltd.(No.J2024163).
文摘With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent,and the accuracy of typical scenario predictions is low.In order to improve the accuracy of scenario prediction under source and load uncertainty,this paper proposes a typical scenario identification model based on random forests and order parameters.Firstly,a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids,taking into account source-load uncertainty.Secondly,the dynamic change characteristics of the order parameters of the daily load curve,wind and solar curve,and load curve of typical scenarios are statistically analyzed to identify the key order parameters that have the most significant impact on the uncertainty of the load.Then,the order parameters and seasonal distribution are used as features to train a random forest classification model to achieve efficient scenario prediction.Finally,the simulation of actual data from a provincial distribution network shows that the proposed method can accurately classify typical scenarios with an accuracy rate of 92.7%.Additionally,sensitivity analysis is conducted to assess how changes in uncertainty levels affect the importance of each order parameter,allowing for adaptive uncertainty mitigation strategies.
基金supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LR25D010003)The Zhejiang Provincial Key Research and Development Program(Grant No.2023C02018)National Natural Science Foundation of China(Grant No.42401400).
文摘The frequent outbreaks of crop diseases pose a serious threat to global agricultural production and food security.Data-driven forecasting models have emerged as an effective approach to support early warning and management,yet the lack of user-friendly tools for model development remains a major bottleneck.This study presents the Multi-Scenario Crop Disease Forecasting Modeling System(MSDFS),an open-source platform that enables end-to-end model construction-from multi-source data ingestion and feature engineering to training,evaluation,and deployment-across four representative scenarios:static point-based,static grid-based,dynamic point-based,and dynamic grid-based.Unlike conventional frameworks,MSDFS emphasizes modeling flexibility,allowing users to build,compare,and interpret diverse forecasting approaches within a unified workflow.A notable feature of the system is the integration of a weather scenario generator,which facilitates comprehensive testing of model performance and adaptability under extreme climatic conditions.Case studies corresponding to the four scenarios were used to validate the system,with overall accuracy(OA)ranging from 73%to 93%.By lowering technical barriers,the system is designed to serve plant protection managers and agricultural producers without advanced programming expertise,providing a practical modeling tool that supports the construction of smart plant protection systems.
文摘Objective:To investigate the effectiveness of standardized patient scenario simulation teaching in geriatric medicine clinical education and provide references for improving teaching methods in geriatrics.Methods:Sixty-five clinical physicians from other departments who rotated into the Geriatric Medicine Department for training between August 2024 and July 2025 were randomly divided into a control group(n=32)and an observation group(n=33).The control group received traditional centralized theoretical lectures combined with instructor-led clinical mentoring,while the observation group underwent standardized patient scenario simulation training.The two groups were compared on post-rotation examination scores and teaching satisfaction metrics.Results:The observation group achieved significantly higher post-rotation examination scores(88.37±3.04)than the control group(80.17±3.29)(p<0.01).Teaching satisfaction surveys revealed that trainees in the observation group demonstrated significantly higher satisfaction than the control group(p<0.05)regarding the teaching method’s effectiveness in enhancing learning interest,independent learning ability,comprehensive clinical problem-solving skills,patient communication skills,teamwork capabilities,and research conceptualization abilities.Conclusion:Standardized patient scenario simulation teaching effectively improves clinical teaching quality in geriatric medicine,enhances trainees’comprehensive clinical competencies,and holds value for broader application.
基金Zhejiang Provincial Sannong-Jiufang Science and Technology Collaboration Initiative,No.2025SNJF012。
文摘With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food trade and security.This study aims to identify the supply-demand match between cropland supply and food consumption and to evaluate sustainable cropland zoning in multiple scenarios and multidimensional assessments.This study uses ecological,environmental and socioeconomic data to quantify diverse food demand patterns into corresponding cropland demands,further mapping the spatio-temporal characteristics of China's cropland supply-demand matches.By utilizing shared socioeconomic pathways(SSPs),this study delineates multiple scenarios to determine the supply-demand of cropland across different Chinese regions from 2030 to 2050.On the basis of ecological,geographical and socioeconomic datasets,this study constructs a multidimensional and multiscenario framework for sustainable agricultural zoning from 2030 to 2050 and proposes a future sustainable agricultural development strategy for each region in different periods.The results indicate that between 2002 and 2022,there was a significant gap between cropland supply and demand.Moreover,an obvious spatial mismatch is observed between cropland supply and demand across various Chinese regions.From 2030 to 2050,there is a noticeable shift in the spatial distribution of cropland supply and demand,with the supply-demand match becoming more strained and varying considerably under different development scenarios.With significant differences between different development scenarios,different regions will have to adopt different development strategies at different periods.This study proposes a multiscenario and multidimensional simulation framework for future agricultural sustainable zoning,which aims to provide scientific insights and policy improvements to promote sustainable agricultural development.
基金Under the auspices of National Natural Science Foundation of China(No.42101251)。
文摘Flood regulation service(FRS)stands as one of the key benefits that people get from the ecosystem.Under the influence of climate change and human activities,the relationship between supply and demand of FRS would increasingly affect regional flood risk and sustainable development.However,there was currently a lack of systematic study on the future supply-demand relationship of FRS in the flood-vulnerable area undergoing rapidly development in China.This study integrated the Scenario Model Intercomparison Project(ScenarioMIP)with the Shared Socioeconomic Pathways(SSPs)datasets and climate model data to quantify the supply-demand ratio(SDR)of FRS in the Yangtze River Delta(YRD),China from 2020 to 2050.Trend analyses were conducted using linear regres-sion,Theil-Sen median estimation,and Hurst exponent analysis,while key drivers of SDR changes were identified and quantified through the Lindeman-Merenda-Gold(LMG)method between 2021 and 2050.Results show that the supply of FRS in the YRD was generally insufficient to meet the demand.The imbalanced subbasins covered 88.24%of the total study area,with 34.48%of this imbal-anced area concentrated in the Southeastern Basin in China.During 2021 and 2050,the imbalance of FRS supply-demand relationship would largely aggravate in the YRD,of which the aggravated area would account for 77.23%.Under different scenarios,the SDR for FRS would decrease significantly,with rates ranging from-5.45×10^(-4) to-2.06×10^(-4)(P<0.05).Especially,the decline rate of SDR in the YRD Basin(DeltaB)reached 2.92 times that the average of YRD.Human activities were the primary factors that exacerbated the imbalance in FRS supply-demand relationship,of which the relative contribution rate exceeds 75%.Particular attention should be direc-ted toward critical regions like the Southeast Basin in China(SEB)and DeltaB where substantial aggravation of supply-demand imbal-ances of FRS is projected.
基金funded by the Science and Technology Project of State Grid Corporation of China under Grant No.5108-202218280A-2-299-XG.
文摘Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R^(2))as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions.
基金funded by National Natural Science Foundation of China(NSFC)(Nos.12075053,11505021 and 11975068)by National Key R&D Program of China(No.2022YFE 03060002)+1 种基金by Fundamental Research Funds for the Central Universities(No.2232024G-10)supported by the U.S.DoE Office of Science(No.DE-FG02–95ER54309)。
文摘Toroidal torques,generated by the resonant magnetic perturbation(RMP)and acting on the plasma column,are numerically systematically investigated for an ITER baseline scenario.The neoclassical toroidal viscosity(NTV),in particular the resonant portion,is found to provide the dominant contribution to the total toroidal torque under the slow plasma flow regime in ITER.While the electromagnetic torque always opposes the plasma flow,the toroidal torque associated with the Reynolds stress enhances the plasma flow independent of the flow direction.A peculiar double-peak structure for the net NTV torque is robustly computed for ITER,as the toroidal rotation frequency is scanned near the zero value.This structure is found to be ultimately due to a non-monotonic behavior of the wave-particle resonance integral(over the particle pitch angle)in the superbanana plateau NTV regime in ITER.These findings are qualitatively insensitive to variations of a range of factors including the wall resistivity,the plasma pedestal flow and the assumed frequency of the rotating RMP field.
基金support provided by the Qingdao Science and Technology Benefits People Demonstration and Guidance Project(21-1-4-sf-4-nsh).
文摘Emergency resources play a vital role in the emergency rescue process.The adequate and timely supply of emergency resources can effectively control the development of accidents and reduce accident losses.However,the current emergency resource allocation of chemical enterprises lacks scientific analysis of accident scenarios,and the individual allocation method of enterprises increases the cost of emergency resource allocation.Given the above problems,this paper proposes a regional collaborative allocation method of emergency resources for enterprises within the chemical industry park(CIP)based on the worst credible accident scenario(WCAS).Firstly,the concept and analysis method of the WCAS is proposed.Then,based on the characteristics and consequences of the accident,the mapping relationship between accident scenarios and emergency resources is established.Finally,an optimization model for regional collaborative allocation of emergency resources is constructed to determine the amount of emergency resource allocation for each enterprise.Through the case study,the emergency resource allocation method based on the WCAS analysis can better meet the demands of accident emergency rescue.Simultaneously,the regional collaborative allocation optimization model can strengthen the cooperation ability among enterprises,greatly reducing the cost of emergency resource allocation for each enterprise.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金supported by the National Natural Science Foun-dation of China(Grants No.42330103,42271469)the Ningbo Science and Technology Bureau(Grant No.2022Z081).
文摘The gap between the projected urban areas in the current trend(UAC)and those in the sustainable scenario(UAS)is a critical factor in understanding whether cities can fulfill the requirements of sustainable development.However,there is a paucity of knowledge on this cutting-edge topic.Given the extensive and rapid urbanization in the United States(U.S.)over the past two centuries,accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development,as well as having significant global implications.This study finds that although the 740 U.S.cities have a large UAC in 2100,these cities will encom pass a significant gap from UAC to UAS(approximately 165,000 km2),accounting for 30%UAC at that time.The study also reveals the spatio-temporal heterogeneity of the gap.The gap initially increases before reaching a inflection point in 2090,and it disparates greatly from−100%to 240%at city level.While cities in the Northwestern U.S.maintain UAC that exceeds UAS from 2020 to 2100,cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS.Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3%to 3%and a 0.68%loss of biomass.Hence,dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization,both within the U.S.and globally.
