4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Floristic composition and edaphic relationships in ferruginous campo rupestre reference ecosystems

Land use change and occupation have led to modifications in the environment causing loss of biodiversity and ecosystem services throughout the planet.Some environments with high economic relevance,such as the ferruginous campo rupestre(rupestrian grassland known as Canga in Brazil),are even more susceptible to severe impacts due to their extreme habitat conditions and low resilience.The determination of reference ecosystems based on the intrinsic characteristics of the ecosystem is essential for conservation as well as to the implementation of ecological restoration.We proposed the reference ecosystem of the three main types of habitats of the ferruginous campo rupestre based on their floristic composition.We described the floristic composition of each habitat and evaluated the physicochemical properties of the soils and the relationship between plants and soils.All three habitats showed high diversity of plant species and many endemic species,such as Chamaecrista choriophylla,Cuphea pseudovaccinium,Lychnophora pinaster,and Vellozia subalata.The distribution of vegetation was strongly related with the edaphic characteristics,with a set of species more adapted to high concentration of base saturation,fine sand,organic carbon,and iron,while another set of species succeeded in more acidic soils with higher S and silt concentration.We provide support for the contention that the ferruginous campo rupestre is a mosaic of different habitats shaped by intrinsic local conditions.Failure to recognize the floristic composition of each particular habitat can lead to inappropriate restoration,increased habitat homogenization and increased loss of biodiversity and ecosystem services.This study also advances the knowledge base for building the reference ecosystem for the different types of ferruginous campo rupestre habitats,as well as a key database for highlighting those species contribute most to community assembly in this diverse and threatened tropical mountain ecosystem.

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

Attosecond ionization time delays in strong-field physics

Electronic processes within atoms and molecules reside on the timescale of attoseconds. Recent advances in the laserbased pump-probe interrogation techniques have made possible the temporal resolution of ultrafast electronic processes on the attosecond timescale, including photoionization and tunneling ionization. These interrogation techniques include the attosecond streak camera, the reconstruction of attosecond beating by interference of two-photon transitions, and the attoclock. While the former two are usually employed to study photoionization processes, the latter is typically used to investigate tunneling ionization. In this review, we briefly overview these timing techniques towards an attosecond temporal resolution of ionization processes in atoms and molecules under intense laser fields. In particular, we review the backpropagation method, which is a novel hybrid quantum-classical approach towards the full characterization of tunneling ionization dynamics. Continued advances in the interrogation techniques promise to pave the pathway towards the exploration of ever faster dynamical processes on an ever shorter timescale.

Exploring the Spatiotemporal Changes and Driving Forces of Ecosystem Services of Zhejiang Coasts,China,Under Sustainable Development Goals

Ecosystem services(ESs)refer to the continuous provisioning of ecosystem goods and services that benefit human beings.Over recent decades,rapid urbanization has exerted significant pressure on coastal ecosystems,resulting in biodiversity and habitat loss,environmental pollution,and the depletion of natural resources.In response to these environmental challenges,the Sustainable Development Goals(SDGs)were proposed.Given the pressing need to address these issues,understanding the changes in ESs under the SDGs is crucial for formulating specific ecological strategies.In this study,we first analyzed land use and cover change in the Zhejiang coasts of China during 2000–2020.Then,we investigated the spatiotemporal configuration of ESs by integrating carbon storage(CS),soil retention(SR),habitat quality(HQ)and water yield(WY)using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model.The driving mechanisms of ESs,which varied by space and time,were also explored using the Geo-detector method.The results revealed that,over the past two decades:1)the Zhejiang coasts have experienced a significant increase of 2783.72 km^(2) in built-up land areas and a continuous decrease in farmland areas due to rapid urbanization;2)owing to higher precipitation,extensive vegetation cover,and reduced anthropogenic disturbances,forests emerge as a crucial land use type for maintaining ecosystem services such as HQ,CS,WY,and SR;3)ESs have generally declined across the entire Zhejiang coasts,with a significant decrease observed in the northern areas and an increase in the southern areas spatially;4)the expansion of built-up land areas emerged as the primary factor affecting ecosystem services,while the vegetation factor has been increasingly significant and is expected to become predominant in the near future.Our study provides insights of understanding of ecosystem service theory and emphasizing the importance of preserving biodiversity for long-term sustainable development,and valuable scientific references to support the ecological management decision-making for local governments.

Unsupervised Time Series Segmentation: A Survey on Recent Advances

Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.

Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau,China

Studying the spatiotemporal variations in ecosystem services and their interrelationships on the Loess Plateau against the background of the gully control and land consolidation(GCLC)project has significant implications for ecological protection and quality development of the Yellow River Basin.Therefore,in this study,we took Yan'an City,Shaanxi Province of China,as the study area,selected four typical ecosystem services,including soil conservation service,water yield service,carbon storage service,and habitat quality service,and quantitatively evaluated the spatiotemporal variation characteristics and trade-offs and synergies of ecosystem services from 2010 to 2018 using the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.We also analysed the relationship between the GCLC project and regional ecosystem service changes in various regions(including 1 city,2 districts,and 10 counties)of Yan'an City and proposed a coordinated development strategy between the GCLC project and the ecological environment.The results showed that,from 2010 to 2018,soil conservation service decreased by 7.76%,while the other three ecosystem services changed relatively little,with water yield service increasing by 0.56% and carbon storage service and habitat quality service decreasing by 0.16% and 0.14%,respectively.The ecological environment of Yan'an City developed in a balanced way between 2010 and 2018,and the four ecosystem services showed synergistic relationships,among which the synergistic relationships between soil conservation service and water yield service and between carbon storage service and habitat quality service were significant.The GCLC project had a negative impact on the ecosystem services of Yan'an City,and the impact on carbon storage service was more significant.This study provides a theoretical basis for the scientific evaluation of the ecological benefits of the GCLC project and the realization of a win-win situation between food security and ecological security.

Evaluation of Human Impacts on Bartlett Pond Ecosystem, Laredo, Southern Texas, USA, through Empirical Modeling

The trace elements chemistry of Bartlett Pond, a small shallow wetland pond in Laredo, Southern Texas, was sampled to evaluate the dynamics of trace elements impacts on water quality and ecosystems ecology of the pond. Two types of fish (bass and tilapia) were also sampled to see the trace element accumulation in different parts of their body. The concentrations of trace elements in water samples were found in the following order: Fe &Gt;Sb > Pb > As &Gt;Co > Tl > Cr > Cd within Bartlett Pond. Overall, the water quality of the pond is unacceptable for drinking and any other purposes as trace element concentrations (e.g. As, Cd, Co, Cr, Pb, Fe, Sb and Tl) are exceedingly higher (several fold) than the WHO and US EPA guidelines. Predictive and correlation analysis shows that most trace elements exhibit a strong positive correlation among them indicating the same anthropogenic sources and biogeochemical processes regulate these trace elements within the pond. Distributions of the trace elements in water exhibit different shapes mostly as positively skewed distribution for As, Cd, Co, Cr, and Tl, symmetrical distribution for Fe and almost symmetrical distribution for Pb and Sb. Concentrations of As, Co and Tl accumulated much higher in different parts of the Bass than Tilapia fish. The concentrations of As, Tl, Co, and Sb appeared significantly higher in different parts of the body of both Bass and Tilapia than the maximum SRM certified values. Accumulation of these contaminants in fish tissues pose increased health risks to humans who consume these contaminated fish although fishing is prohibited. Anthropogenic activities in the region primarily degrade the whole pond ecosystem ecology of the Bartlett Pond and waters of this pond to be not recommended for any use. These findings may be useful for the scientific community and concerned authorities to improve understanding about these precious natural resources and conservation of the ecosystem ecology.

Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and microenvironment.Breast cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and differentiation.Studies have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast cancer.The tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ability.Hence,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer metastasis.In this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory mechanisms.Furthermore,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during metastasis.Overall,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

Spatio-temporal evolution and topographic gradient effect of land use and ecosystem service value in the Lhasa River Basin

The Lhasa River Basin forms an essential human settlement area in the southern part of the Qinghai-Tibet Plateau.This study employed ecosystem service value(ESV)evaluation model,terrain gradient grading,and Geodetector to analyze land use and ESV in the Lhasa River Basin from 1985 to 2020.The findings reveal that:(1)From 1985 to 2020,grassland was the dominant land use.There was a trend of grassland reduction and the expansion of other land types.(2)ESV has increased over the research period(with a total increase of 0.84%),with higher values in the southeast and lower values in the northwest.Grassland contributed the most to ESV,and climate regulation and hydrological regulation were the ecosystem services that contribute the most to ESV.(3)Natural factors like NDVI and altitude,as well as economic factors like population density and distance from roads,influenced the spatial differentiation of ESV,the explanatory power of NDVI reached up to 0.47.The interaction between factors had a greater impact than individual factors.These research results can provide theoretical support for national spatial planning and ecological environment protection in the Lhasa River Basin and other similar areas.

J-family genes redundantly regulate flowering time and increase yield in soybean

Soybean(Glycine max)is a short-day crop whose flowering time is regulated by photoperiod.The longjuvenile trait extends its vegetative phase and increases yield under short-day conditions.Natural variation in J,the major locus controlling this trait,modulates flowering time.We report that the three J-family genes influence soybean flowering time,with the triple mutant Guangzhou Mammoth-2 flowering late under short days by inhibiting transcription of E1-family genes.J-family genes offer promising allelic combinations for breeding.

Dispersion Relations in Diffraction in Time

In agreement with Titchmarsh’s theorem, we prove that dispersion relations are just the Fourier-transform of the identity, g(x′)=±Sgn(x′)g(x′), which defines the property of being a truncated functions at the origin. On the other hand, we prove that the wave-function of a generalized diffraction in time problem is just the Fourier-transform of a truncated function. Consequently, the existence of dispersion relations for the diffraction in time wave-function follows. We derive these explicit dispersion relations.

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Grassland-type ecosystem stability in China differs under the influence of drought and wet events

Ecological stability is a core issue in ecological research and holds significant implications forhumanity. The increased frequency and intensity of drought and wet climate events resulting from climatechange pose a major threat to global ecological stability. Variations in stability among different ecosystemshave been confirmed, but it remains unclear whether there are differences in stability within the sameterrestrial vegetation ecosystem under the influence of climate events in different directions and intensities.China's grassland ecosystem includes most grassland types and is a good choice for studying this issue.This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify thedirections and intensities of different types of climate events, and based on Normalized DifferenceVegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measurestability). Based on a traditional regression model, standardized methods were integrated to analyze theimpacts of the intensity and duration of drought and wet events on vegetation stability. The resultsshowed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had thelowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at anintermediate level. Regarding the impact of the duration and intensity of climate events on vegetationecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainlyaffected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppewas mainly affected by intensity in wet environments, and duration had no significant impact. Ourconclusions can provide decision support for the future grassland ecosystem governance.