Salinity is one of the major abiotic stresses limiting chickpea(Cicer arietinum L.)productivity,particularly in arid and semi-arid regions where soil salinization is intensifying.Developing cost-effective and practica...Salinity is one of the major abiotic stresses limiting chickpea(Cicer arietinum L.)productivity,particularly in arid and semi-arid regions where soil salinization is intensifying.Developing cost-effective and practical strategies to enhance seedling establishment and early vigor under saline conditions is therefore essential.In this study,we compared two seed-priming agents-1 mM proline and 25 mM NaCl-under identical hydroponic conditions to elucidate tissue-specific responses to 25 mM NaCl stress.Proline priming significantly improved shoot length(by~23%),total chlorophyll content(by~19%),and ascorbate peroxidase(ASPOX)activity.In contrast,NaCl priming enhanced root biomass retention(by~38%)and peroxidase(POD)activity under salinity stress.Both priming treatments induced higher proline accumulation and antioxidant capacity,though with tissue-specific effects:proline favored aboveground resilience,while NaCl strengthened root ionic and oxidative balance.These findings highlight the complementary nature of proline and NaCl priming and support the concept of stress“memory,”whereby plants acquire enhanced readiness to cope with salinity.Integrating such priming strategies into chickpea cultivation could contribute to improved yield stability and sustainability in saline agroecosystems.展开更多
Harmonic mode-locking(HML)in soliton fiber lasers is crucial for generating high-repetition-rate pulse trains beyond the fundamental cavity frequency,enabling advanced applications in,for example,optical communication...Harmonic mode-locking(HML)in soliton fiber lasers is crucial for generating high-repetition-rate pulse trains beyond the fundamental cavity frequency,enabling advanced applications in,for example,optical communication and precision sensing.However,achieving HML in experiments is challenging,owing to its inherent instability and high sensitivity to laser parameters,resulting in complex and iterative adjustments.In this paper,a novel HML technique utilizing bidirectional adjustment of pump power is proposed,and it is experimentally demonstrated in an all-fiber hybrid mode-locked soliton laser.By first increasing the pump power to generate a soliton bunch with a certain number of pulses and then gradually decreasing it,HML can be achieved at an order corresponding to the number of pulses in the soliton bunch.Experimental results on the evolution of temporal pulse trains during bidirectional adjustment of the pump power enable a relationship to be established between pump power and soliton bunching with increasing pump power,and reveal the collapse of the soliton bunch and subsequent gradual uniform distribution of solitons into an HML state with decreasing pump power.Second-to sixth-order HML is successfully generated using the proposed technique,and an analysis of the results provides a deeper understanding of the observed pulse dynamics.展开更多
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio...The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.展开更多
Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation gener...Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.展开更多
In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a ...In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies...Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.展开更多
The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Diff...The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.展开更多
Programmable/reprogrammable magneto-responsive composites(MRCs)are highly desirable for applications in soft robotics,morphable actuators,and biomedical devices due to their capabilities of undergoing reversible,compl...Programmable/reprogrammable magneto-responsive composites(MRCs)are highly desirable for applications in soft robotics,morphable actuators,and biomedical devices due to their capabilities of undergoing reversible,complex,untethered,and rapid deformations.However,current MRC-based devices primarily rely on soft matrices,which revert to their original shapes and cease functioning when external magnetic fields are removed.Moreover,their magnetization programming,deformations,and functioning need to alternate between encoding and actuation platforms,limiting the adaptability and efficiency.Here,we present a reprogrammable magnetic shape-memory composite(RM-SMC)integrating a shape-memory polymer(SMP)skeleton with phase-transition magnetic microcapsules.High-intensity laser melts microcapsules for magnetic realignment under programmed fields,while low-intensity laser softens SMP for structural reconfiguration without compromising integrity.This dual-laser strategy facilitates in situ magnetization programming,shape morphing,and function execution within a single material system.Our innovative approach enables unique applications,including omnidirectional multi-degree-of-freedom actuators that can activate light switches,solar trackers that optimize energy capture,and adaptive impellers that modulate fluid pumping.By eliminating platform alternation and enabling shape/function retention post-actuation,the RM-SMC platform overcomes critical limitations in conventional MRCs,establishing a paradigm for multifunctional devices requiring persistent configuration control and field-independent operation.展开更多
The world economy is now in a period of indepth adjustment.The intensification of geopolitical conflicts,a rise of trade protectionism,and reconfiguration of global supply chains have prompted all countries to re-exam...The world economy is now in a period of indepth adjustment.The intensification of geopolitical conflicts,a rise of trade protectionism,and reconfiguration of global supply chains have prompted all countries to re-examine their openness to trade amid rising uncertainties.Against this backdrop,China’s continuing commitment to promoting higherlevel opening up and building the Hainan Free Trade Port(Hainan FTP)has distinct contemporary significance and strategic value.展开更多
To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.T...To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.展开更多
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.展开更多
Local mate competition theory predicts that offspring sex ratio in pollinating fig wasps is female-biased when there is only one foundress, and increased foundress density results in increased offspring sex ratio. Inf...Local mate competition theory predicts that offspring sex ratio in pollinating fig wasps is female-biased when there is only one foundress, and increased foundress density results in increased offspring sex ratio. Information of other foundresses and clutch size have been suggested to be the main proximate explanations for sex ratio adjustment under local mate competition. Our focus was to show the mechanism of sex ratio adjustment in a pollinating fig wasp, Ceratosolen solmsi Mayr, an obligate pollinator of the functionally dioecious fig, Ficus hispida Linn., with controlled experiments in the field. First, we obtained offspring from one pollinator and offspring at different oviposition sequences, and found that offspring sex ratio decreased with clutch size, and pollinators produced most of their male offspring at the start of bouts, followed by mostly females. Second, we found that offspring sex ratio increased with foundress density, and pollinators did adjust their offspring sex ratio to other females in the oviposition patches. We suggest that when oviposition sites are not limited, pollinators will mainly adjust their offspring sex ratio to other foundresses independent of clutch size changes, whereas adjusting clutch size may be used to adjust sex ratio when oviposition sites are limited.展开更多
Activeow control technology is a technique that controls the internaloweld of aircraft engines or theoweld around wings by means of disturbances induced by actuators,and adjusts the aerodynamic force and attitude of t...Activeow control technology is a technique that controls the internaloweld of aircraft engines or theoweld around wings by means of disturbances induced by actuators,and adjusts the aerodynamic force and attitude of the aircraft,so as to achieve the purposes of increasing lift,reducing drag,suppressing vibration and reducing noise.Hailed as an important source of innovative development for aircraft,this technology provides a new technical approach to solve the aerodynamic problems of aircraft,signicantly improve their comprehensive performance,break throughight boundaries,and promote disruptive innovation in the next generation of aircraft.展开更多
Solar radiation modification,a scheme aimed at mitigating rapid global warming triggered by anthropogenic greenhouse gas emissions,has been explored through the G1ext experiment under the Geoengineering Model Intercom...Solar radiation modification,a scheme aimed at mitigating rapid global warming triggered by anthropogenic greenhouse gas emissions,has been explored through the G1ext experiment under the Geoengineering Model Intercomparison Project(GeoMIP) framework,utilizing the Chinese Academy of Sciences Earth System Model version 2(CAS-ESM2.0).This paper briefly describes the basic configuration and experimental design of the CAS-ESM2.0 for G1ext,which involves a sudden reduction in solar irradiance to counterbalance the radiative forcing of an abrupt quadrupling of atmospheric CO_(2) concentration,running for 100 years.Preliminary results show that this model can reproduce well the compensatory effect of a uniform decrease in global solar radiation on the radiative forcing resulting from an abrupt quadrupling of CO_(2) concentration.Like other Earth system models,CAS-ESM2.0 reasonably captures variations in radiative adjustments,surface air temperature,and precipitation patterns,both globally and locally,under the G1ext scenario.The generated datasets have been released on the Earth System Grid Federation data server,providing insight into the potential efficacy and impact of solar geoengineering strategies.展开更多
The Yangtze River Delta(YRD)region has witnessed a consistent decrease in NO_(2),CO,and PM_(2.5) from 2016 to 2023.However,ozone has exhibited fluctuating patterns.Quantifying ozone contributions from emissions,both w...The Yangtze River Delta(YRD)region has witnessed a consistent decrease in NO_(2),CO,and PM_(2.5) from 2016 to 2023.However,ozone has exhibited fluctuating patterns.Quantifying ozone contributions from emissions,both within and outside the YRD,is essential for understanding city-cluster-scale ozone pollution(CCSOP).To address these concerns,a comprehensive approach combining Kolmogorov-Zurbenko filtering,Empirical Orthogonal Function,Absolute Principal Component Score,andMultiple Linear Regression methods(KZ-EOF-APCs-MLR)was employed to quantify the impacts of meteorological factors,local and non-local emission contributions of ozone(LECO and NECO).Emission changes were identified as the predominant factor shaping annual fluctuations in ambient ozone.Notably,during the previous andmiddle stages of the COVID-19 pandemic(from2017 to 2021),emissions reductions led to a marked decrease in YRD ozone levels(-7.01μg/m^(3)),with a pronounced rebound post-pandemic(2022 to 2023)(+8.04μg/m^(3)).Seasonally,the emissioninduced ozone exhibited fluctuating upward trend during autumn and winter,suggesting a transition of ozone pollution towards colder seasons.Spatially,high LECO concentrated in the eastern YRD(EYRD)across spring,autumn,and winter,becoming prominent in the central YRD(CYRD)during summer.During CCSOP,the CYRD exhibited the highest LECO and exceedance frequency(20.82μg/m^(3) and 45.27%).LECO explained a large portion of ozone variability during CCSOP,particularly in the EYRD,while NECO showed less explanatory power but consistently high contributions(148.05±15.52μg/m^(3)).These findings offer valuable insights for a deeper understanding of the evolving patterns of ozone pollution and the issue of CCSOP in the YRD.展开更多
In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in ...In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery.By establishing a comprehensive big data analysis platform,the flowback dynamics of over 1000 shale gas wells were analyzed in this work,leading to the development of an index system for evaluating flowback production capacity.Additionally,a shut-in chart was created for wells with different types of post-stimulation fracture networks,providing a structured approach to optimizing production strategies.A dynamic analysis method for flowback was also developed,using daily pressure drop and artificial fracture conductivity as key indicators.This method offers a systematic and effective approach to managing the shut-in and flowback processes for gas wells.Field trials demonstrated significant improvements:the probability of sand production was reduced,gas breakthrough time was extended,artificial fracture conductivity was enhanced,and the average estimated ultimate recovery(EUR)per well increased.展开更多
Current mix design practices typically assume total blending and use the white curve of reclaimed asphalt pavement(RAP)to determine the gradation and optimum asphalt content(OAC)of recycled hot mix asphalt(HMA),often ...Current mix design practices typically assume total blending and use the white curve of reclaimed asphalt pavement(RAP)to determine the gradation and optimum asphalt content(OAC)of recycled hot mix asphalt(HMA),often overlooking the effects of RAP agglomeration and partial blending.This oversight can result in unsatisfactory performance,particularly when higher RAP content is used.Therefore,this paper reviews and discusses strategies for adjusting the mix design of recycled HMA to enhance its in-service performance.The discussion begins with RAP particle agglomeration,a significant phenomenon that significantly impacts the aggregate gradation of recycled HMA.Subsequently,detection methods to clarify the blending between virgin and RAP binders are described.Partial blending between RAP and virgin binders is common,and various indexes have been proposed to quantify the blending degree.Finally,the adjusted mix design method of recycled HMA is presented,emphasizing gradation optimization and corrected OAC.Gradation optimization should account for RAP agglomeration,while the corrected OAC should consider particle blending.Recycled HMA using the adjusted mix design exhibits improved crack resistance and fatigue life without substantially impairing rutting performance.This review aims to help both academics and highway agencies maximize the utilization of RAP materials within sustainable pavement frameworks.展开更多
文摘Salinity is one of the major abiotic stresses limiting chickpea(Cicer arietinum L.)productivity,particularly in arid and semi-arid regions where soil salinization is intensifying.Developing cost-effective and practical strategies to enhance seedling establishment and early vigor under saline conditions is therefore essential.In this study,we compared two seed-priming agents-1 mM proline and 25 mM NaCl-under identical hydroponic conditions to elucidate tissue-specific responses to 25 mM NaCl stress.Proline priming significantly improved shoot length(by~23%),total chlorophyll content(by~19%),and ascorbate peroxidase(ASPOX)activity.In contrast,NaCl priming enhanced root biomass retention(by~38%)and peroxidase(POD)activity under salinity stress.Both priming treatments induced higher proline accumulation and antioxidant capacity,though with tissue-specific effects:proline favored aboveground resilience,while NaCl strengthened root ionic and oxidative balance.These findings highlight the complementary nature of proline and NaCl priming and support the concept of stress“memory,”whereby plants acquire enhanced readiness to cope with salinity.Integrating such priming strategies into chickpea cultivation could contribute to improved yield stability and sustainability in saline agroecosystems.
基金supported by the National Natural Science Foundation of China(Grant No.52475574).
文摘Harmonic mode-locking(HML)in soliton fiber lasers is crucial for generating high-repetition-rate pulse trains beyond the fundamental cavity frequency,enabling advanced applications in,for example,optical communication and precision sensing.However,achieving HML in experiments is challenging,owing to its inherent instability and high sensitivity to laser parameters,resulting in complex and iterative adjustments.In this paper,a novel HML technique utilizing bidirectional adjustment of pump power is proposed,and it is experimentally demonstrated in an all-fiber hybrid mode-locked soliton laser.By first increasing the pump power to generate a soliton bunch with a certain number of pulses and then gradually decreasing it,HML can be achieved at an order corresponding to the number of pulses in the soliton bunch.Experimental results on the evolution of temporal pulse trains during bidirectional adjustment of the pump power enable a relationship to be established between pump power and soliton bunching with increasing pump power,and reveal the collapse of the soliton bunch and subsequent gradual uniform distribution of solitons into an HML state with decreasing pump power.Second-to sixth-order HML is successfully generated using the proposed technique,and an analysis of the results provides a deeper understanding of the observed pulse dynamics.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the National Natural Science Foundation of China(Grant No.U2142213)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grants 2025A1515011835,2022A1515011870)the National Natural Science Foundation of China(Grant No.42305167)。
文摘The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.
基金supported by the National Key Research and Development Program of China(2020AAA0109300)the Shanghai Collaborative Innovation Center of data intelligence technology(No.0232-A1-8900-24-13).
文摘Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13。
文摘In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the National Natural Science Foundation of China(Grant No.62106092)the Natural Science Foundation of Fujian Province(Grant Nos.2024J01822,2025J01981)the Natural Science Foundation of Zhangzhou City(Grant No.ZZ2024J28).
文摘Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.
基金supported by the United Kingdom(UK)Darwin Initiative(28-003).
文摘The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.
基金supported by the National Natural Science Foundation of China(Nos.52075516,61927814,62325507,and 52122511)the National Key Research and Development Program of China(No.2021YFF0502700)+2 种基金the Major Scientific and Technological Projects in Anhui Province(202103a05020005,202203a05020014)the Students’Innovation and Entrepreneurship Foundation of USTC(CY2022G09)the Hefei Municipal Natural Science Foundation(No.HZR2450)。
文摘Programmable/reprogrammable magneto-responsive composites(MRCs)are highly desirable for applications in soft robotics,morphable actuators,and biomedical devices due to their capabilities of undergoing reversible,complex,untethered,and rapid deformations.However,current MRC-based devices primarily rely on soft matrices,which revert to their original shapes and cease functioning when external magnetic fields are removed.Moreover,their magnetization programming,deformations,and functioning need to alternate between encoding and actuation platforms,limiting the adaptability and efficiency.Here,we present a reprogrammable magnetic shape-memory composite(RM-SMC)integrating a shape-memory polymer(SMP)skeleton with phase-transition magnetic microcapsules.High-intensity laser melts microcapsules for magnetic realignment under programmed fields,while low-intensity laser softens SMP for structural reconfiguration without compromising integrity.This dual-laser strategy facilitates in situ magnetization programming,shape morphing,and function execution within a single material system.Our innovative approach enables unique applications,including omnidirectional multi-degree-of-freedom actuators that can activate light switches,solar trackers that optimize energy capture,and adaptive impellers that modulate fluid pumping.By eliminating platform alternation and enabling shape/function retention post-actuation,the RM-SMC platform overcomes critical limitations in conventional MRCs,establishing a paradigm for multifunctional devices requiring persistent configuration control and field-independent operation.
文摘The world economy is now in a period of indepth adjustment.The intensification of geopolitical conflicts,a rise of trade protectionism,and reconfiguration of global supply chains have prompted all countries to re-examine their openness to trade amid rising uncertainties.Against this backdrop,China’s continuing commitment to promoting higherlevel opening up and building the Hainan Free Trade Port(Hainan FTP)has distinct contemporary significance and strategic value.
基金supported by the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2024ZD0302502 for WZ)the National Natural Science Foundation of China(Grant No.92365210 for WZ)+1 种基金Tsinghua Initiative Scientific Research Program (for WZ)the project of Tsinghua University-Zhuhai Huafa Industrial Share Company Joint Institute for Architecture Optoelectronic Technologies (JIAOT,for YH)。
文摘To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
文摘Local mate competition theory predicts that offspring sex ratio in pollinating fig wasps is female-biased when there is only one foundress, and increased foundress density results in increased offspring sex ratio. Information of other foundresses and clutch size have been suggested to be the main proximate explanations for sex ratio adjustment under local mate competition. Our focus was to show the mechanism of sex ratio adjustment in a pollinating fig wasp, Ceratosolen solmsi Mayr, an obligate pollinator of the functionally dioecious fig, Ficus hispida Linn., with controlled experiments in the field. First, we obtained offspring from one pollinator and offspring at different oviposition sequences, and found that offspring sex ratio decreased with clutch size, and pollinators produced most of their male offspring at the start of bouts, followed by mostly females. Second, we found that offspring sex ratio increased with foundress density, and pollinators did adjust their offspring sex ratio to other females in the oviposition patches. We suggest that when oviposition sites are not limited, pollinators will mainly adjust their offspring sex ratio to other foundresses independent of clutch size changes, whereas adjusting clutch size may be used to adjust sex ratio when oviposition sites are limited.
文摘Activeow control technology is a technique that controls the internaloweld of aircraft engines or theoweld around wings by means of disturbances induced by actuators,and adjusts the aerodynamic force and attitude of the aircraft,so as to achieve the purposes of increasing lift,reducing drag,suppressing vibration and reducing noise.Hailed as an important source of innovative development for aircraft,this technology provides a new technical approach to solve the aerodynamic problems of aircraft,signicantly improve their comprehensive performance,break throughight boundaries,and promote disruptive innovation in the next generation of aircraft.
基金supported by the National Natural Science Foundation of China(Grant No.41875126)the National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility”(EarthLab)。
文摘Solar radiation modification,a scheme aimed at mitigating rapid global warming triggered by anthropogenic greenhouse gas emissions,has been explored through the G1ext experiment under the Geoengineering Model Intercomparison Project(GeoMIP) framework,utilizing the Chinese Academy of Sciences Earth System Model version 2(CAS-ESM2.0).This paper briefly describes the basic configuration and experimental design of the CAS-ESM2.0 for G1ext,which involves a sudden reduction in solar irradiance to counterbalance the radiative forcing of an abrupt quadrupling of atmospheric CO_(2) concentration,running for 100 years.Preliminary results show that this model can reproduce well the compensatory effect of a uniform decrease in global solar radiation on the radiative forcing resulting from an abrupt quadrupling of CO_(2) concentration.Like other Earth system models,CAS-ESM2.0 reasonably captures variations in radiative adjustments,surface air temperature,and precipitation patterns,both globally and locally,under the G1ext scenario.The generated datasets have been released on the Earth System Grid Federation data server,providing insight into the potential efficacy and impact of solar geoengineering strategies.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,41975037)the National Key Research and Development Programof China(No.2022YFC3700303).
文摘The Yangtze River Delta(YRD)region has witnessed a consistent decrease in NO_(2),CO,and PM_(2.5) from 2016 to 2023.However,ozone has exhibited fluctuating patterns.Quantifying ozone contributions from emissions,both within and outside the YRD,is essential for understanding city-cluster-scale ozone pollution(CCSOP).To address these concerns,a comprehensive approach combining Kolmogorov-Zurbenko filtering,Empirical Orthogonal Function,Absolute Principal Component Score,andMultiple Linear Regression methods(KZ-EOF-APCs-MLR)was employed to quantify the impacts of meteorological factors,local and non-local emission contributions of ozone(LECO and NECO).Emission changes were identified as the predominant factor shaping annual fluctuations in ambient ozone.Notably,during the previous andmiddle stages of the COVID-19 pandemic(from2017 to 2021),emissions reductions led to a marked decrease in YRD ozone levels(-7.01μg/m^(3)),with a pronounced rebound post-pandemic(2022 to 2023)(+8.04μg/m^(3)).Seasonally,the emissioninduced ozone exhibited fluctuating upward trend during autumn and winter,suggesting a transition of ozone pollution towards colder seasons.Spatially,high LECO concentrated in the eastern YRD(EYRD)across spring,autumn,and winter,becoming prominent in the central YRD(CYRD)during summer.During CCSOP,the CYRD exhibited the highest LECO and exceedance frequency(20.82μg/m^(3) and 45.27%).LECO explained a large portion of ozone variability during CCSOP,particularly in the EYRD,while NECO showed less explanatory power but consistently high contributions(148.05±15.52μg/m^(3)).These findings offer valuable insights for a deeper understanding of the evolving patterns of ozone pollution and the issue of CCSOP in the YRD.
基金PetroChina Research Applied Science and Technology Project,“Shale Gas Scale Increase Production and Exploration andDevelopment Technology-Research and Application of Key Technology of Deep Shale Gas Scale Production”(No.2023ZZ21YJ01).
文摘In the context of post-stimulation shale gas wells,the terms“shut-in”and“flowback”refer to two critical phases that occur after hydraulic fracturing(fracking)has been completed.These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery.By establishing a comprehensive big data analysis platform,the flowback dynamics of over 1000 shale gas wells were analyzed in this work,leading to the development of an index system for evaluating flowback production capacity.Additionally,a shut-in chart was created for wells with different types of post-stimulation fracture networks,providing a structured approach to optimizing production strategies.A dynamic analysis method for flowback was also developed,using daily pressure drop and artificial fracture conductivity as key indicators.This method offers a systematic and effective approach to managing the shut-in and flowback processes for gas wells.Field trials demonstrated significant improvements:the probability of sand production was reduced,gas breakthrough time was extended,artificial fracture conductivity was enhanced,and the average estimated ultimate recovery(EUR)per well increased.
基金sponsored by the National Natural Science Foundation of China(52178420,52408476)Special subsidy from Heilongjiang Provincial People's Government(HITTY-20190028)+1 种基金Postdoctoral Fellowship Program of CPSF(GZC20242207)the Fundamental Research Funds for the Central Universities(HIT.DZJJ.2023086).
文摘Current mix design practices typically assume total blending and use the white curve of reclaimed asphalt pavement(RAP)to determine the gradation and optimum asphalt content(OAC)of recycled hot mix asphalt(HMA),often overlooking the effects of RAP agglomeration and partial blending.This oversight can result in unsatisfactory performance,particularly when higher RAP content is used.Therefore,this paper reviews and discusses strategies for adjusting the mix design of recycled HMA to enhance its in-service performance.The discussion begins with RAP particle agglomeration,a significant phenomenon that significantly impacts the aggregate gradation of recycled HMA.Subsequently,detection methods to clarify the blending between virgin and RAP binders are described.Partial blending between RAP and virgin binders is common,and various indexes have been proposed to quantify the blending degree.Finally,the adjusted mix design method of recycled HMA is presented,emphasizing gradation optimization and corrected OAC.Gradation optimization should account for RAP agglomeration,while the corrected OAC should consider particle blending.Recycled HMA using the adjusted mix design exhibits improved crack resistance and fatigue life without substantially impairing rutting performance.This review aims to help both academics and highway agencies maximize the utilization of RAP materials within sustainable pavement frameworks.
