The Influence of Vulnerable Narcissism on Social Anxiety among Adolescents:The Mediating Role of Self-Concept Clarity and Self-Esteem

Social anxiety (SA) is a prevalent mental health issue among adolescents, and vulnerable narcissism (VN) can exacerbate thiscondition. This study aims to investigate the impact of vulnerable narcissism on social anxiety in adolescents, specificallyfocusing on the mediating effects of self-concept clarity (SCC) and self-esteem (SE) in the relationship between vulnerablenarcissism and social anxiety. Through cluster sampling, a questionnaire survey was conducted among 982 students from threesecondary schools in two provinces. The data was analyzed using structural equation modeling (SEM). The results revealedthat there was a significant negative correlation between vulnerable narcissism and both self-concept clarity and self-esteem,while there was a significant positive correlation between vulnerable narcissism and social anxiety. Additionally, self-conceptclarity showed a significant positive correlation with self-esteem but had a negative correlation with social anxiety. Both selfconceptclarity and self-esteem played an intermediary role in the chain linking vulnerable narcissism to social anxiety. Thisstudy confirms the mediating role of both self-concept clarity and self-esteem in explaining how vulnerable narcissisminfluences social anxiety, providing valuable insights into its underlying mechanism.

Spatial Optimization Strategies for High Temperature Heat Exposure Based on Thermally Vulnerable Populations and Case Studies

The objective of this study is to investigate the factors that contribute to brittleness and to identify strategies for mitigating these factors in populations with varying degrees of thermal vulnerability,based on the potential impact of extreme heat exposure on human survival and habitability.The physiological condition of lower adaptability to high temperature environments and the assessment of individuals who may have higher tolerance time in high temperature environments based on spatial perspectives suggest the need for targeted spatial optimization strategies for commuters and disadvantaged populations.This is demonstrated through a case study.These optimization measures encompass a variety of aspects,including the integration of transportation systems,the expansion of grey space corridors,the improvement of green space layout,and the implantation of green infrastructure.The study aims to reduce the exposure time of thermally vulnerable individuals to high temperature environments through spatial optimization strategies,to enhance the resilience of urban green spaces to heat stress,and to reduce the probability of heat-wave occurrence.

Eco-Environmental Vulnerability Evaluation in the Yellow River Basin,China

Using remote sensing(RS)data and geographical information system(GIS),eco-environmental vulnerability and its changes were analyzed for the Yellow River Basin,China.The objective of this study was to improve our understanding of eco-environmental changes so that a strategy of sustainable land use could be established.An environmental numerical model was developed using spatial principal component analysis(SPCA)model.The model contains twelve factors that include variables of land use,soil erosion,topography,climate,and vegetation.Using this model,synthetic eco- environmental vulnerability index(SEVI)was computed for 1990 and 2000 for the Yellow River Basin.The SEVI was classified into six levels,potential,slight,light,medium,heavy,and very heavy,following the natural breaks classification. The eco-environmental vulnerability distribution and its changes over the ten years from 1990 to 2000 were analyzed and the driving factors of eco-environmental changes were investigated.The results show that the eco-environmental vulnerability in the study area was at medium level,and the eco-environmental quality had been gradually improved on the whole.However,the eco-environmental quality had become worse over the ten years in some regions.In the study area,population growth,vegetation degradation,and governmental policies for eco-environmental protection were found to be the major factors that caused the eco-environmental changes over the ten years.

Are vulnerable farmers more easily influenced?Heterogeneous effects of Internet use on the adoption of integrated pest management

The Internet is believed to bring more technological dividends to vulnerable farmers during the green agriculture transformation.However,this is different from the theory of skill-biased technological change,which emphasizes that individuals with higher levels of human capital and more technological endowments benefit more.This study investigates the effects of Internet use on farmers'adoption of integrated pest management(IPM),theoretically and empirically,based on a dataset containing 1015 farmers in China's Shandong Province.By exploring the perspective of rational inattention,the reasons for the heterogeneity of the effects across farmers with different endowments,i.e.,education and land size,are analyzed.The potential endogeneity issues are addressed using the endogenous switching probit model.The results reveal that:(1)although Internet use significantly positively affects farmers'adoption of IPM,vulnerable farmers do not benefit more from it.Considerable selection bias leads to an overestimation of technological dividends for vulnerable farmers;(2)different sources of technology information lead to the difference in the degree of farmers'rational inattention toward Internet information,which plays a crucial role in the heterogeneous effect of Internet use;and(3)excessive dependence on strong-tie social network information sources entraps vulnerable farmers in information cocoons,hindering their ability to reap the benefits of Internet use fully.Therefore,it is essential to promote services geared towards elderly-oriented Internet agricultural technology information and encourage farmers with strong Internet utilization skills to share technology information with other farmers actively.

Evaluation model of the grey fuzzy on eco-environment vulnerability

The basic theory and evaluation index system of eco-environment vulnerability were reviewed. Based on the grey theory and fuzzy mathematics, a new comprehensive evaluation method from qualitative to quantitative, called grey-fuzzy evaluation, was proposed for evaluating eco-environment vulnerability. It was integrated of Association for Healthcare Philanthropy （AHP）, grey correlation analysis, grey statistics and fuzzy judgment. The constitutional principle and method of the new evaluation method were given and its feasibility and effectiveness were proved by the practical example.

Comprehensive Assessment of Eco-environment Vulnerability in Hebei Province Based on ArcGIS

[Objective] This study aimed to assess the vulnerability of ecological environment in Hebei Province.[Method] Based on ArcGIS,by using the dominant factor and maximum limits factor method,we established the sensitivity-reality indicator system and assessment model of the eco-environment vulnerability in Hebei Province to quantitatively evaluate its eco-environment vulnerability,and analyzed its spatial distribution.[Result] The status quo of environmental degradation was inconsistent with the sensitivity of eco-environment in Hebei Province.The area of severely vulnerable region accounted for only 4.1% of total area in Hebei Province,mainly distributed in nine counties or districts of Baxia region,Zhangjiakou,northwestern Hebei,which was covered with mountains and basins,showing bands,which resulted from the vulnerability of soil erosion and land desertification.In addition,the moderately vulnerable region made up 38.4%,having the largest proportion,and mainly distributed in the mountain and basin region in northwestern Hebei,Yanshan Mountain and the most regions of Taihang Mountain,due to the vulnerability of soil erosion.At the same time,there were also large patches of moderately vulnerable region in Bashang and Baxia of Zhangjiakou,owning to the vulnerability of land desertification.Besides,most plains were mildly vulnerable and slightly vulnerable regions,and their areas accounted for 25.1% and 32.4% respectively of total area.[Conclusion] The research could provide scientific references for safeguarding the ecological security and main functional district planing of Hebei Province.

Natural Barriers to Eco-environmental Vulnerability in a Complex Ecosystem

Natural features such as mountain ranges, steep slopes and vegetation prevent human movement from one habitat to another. They prevent the ecological harm from natural phenomenon like erosion and landslide. Forests destruction has brought about deterioration of ecological environment such as increasing soil and water losses. RS （Remote Sensing） and GIS （Geographic Information System） technology have enhanced the eco-environment assessment procedure using eco-environment quality index tool. This paper presents results of the research on the investigation of the potentials of different landscapes on the complex ecosystem of Makeng Village in Fuj Jan Province to act as natural barrier to eco-environmental vulnerability. Vulnerability factors analysed were soil erosion, vegetation cover, land use types, slope and elevation. To see how one factor acts as natural barrier eco-environment stressors, factor maps were overlaid in pairs using ArcGIS 9.2 software and the matrix statistics exported for analysis in Microsoft Excel. The results showed steep slopes naturally limit human activities, growth of big trees and increase soil erosion. Flat and gentle slopes are less vulnerable to erosion. Elevation is among natural barriers to human activities. Human activities decrease with increasing elevation, hence making the eco-environment naturally stable/undisturbed. In this study, eco-vulnerability to erosion decreases with increasing vegetation cover.

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

Socio-economic vulnerability level in the Jeneberang watershed in Gowa Regency,South Sulawesi Province,Indonesia

Jeneberang watershed is vital,particularly for people living in Gowa Regency(South Sulawesi Province,Indonesia),who benefit from its many advantages.Landslides and floods occur every year in the Jeneberang watershed,so it is imperative to understand the socio-economic vulnerability of this region.This research aims to identify the vulnerability level of the Jeneberang watershed so that the government can prioritize areas with high vulnerability level and formulate effective strategies to reduce these the vulnerability.Specifically,this study was conducted in 12 districts located in the Jeneberang watershed.The primary data were collected from questionnaires completed by community members,community leaders,and various stakeholders,and the secondary data were from the Landsat satellite imagery in 2020,the Badan Push Statistic of Gowa Regency,and some governmental agencies.The socio-economic vulnerability variables were determined using the Multiple Criteria Decision Analysis(MCDA)method,and each variable was weighted and analyzed using the Geographical Information System(GIS).The study reveals that the levels of socio-economic vulnerability are affected by variables such as population density,vulnerable groups(disabled people,elderly people,and young people),road network and settlement,percentage of poor people,and productive land area in the Jeneberang watershed.Moreover,all of the 12 districts in the Jeneberang watershed are included in the medium vulnerability level,with the mean percentage of socio-economic vulnerability around 50.92%.The socio-economic vulnerability of Bajeng,Pallangga,and Somba Opu districts is categorized at high level,the socio-economic vulnerability of Bungaya,Parangloe,and Tombolo Pao districts is classified as medium level,and the remaining 6 districts(Barombong,Bontolempangan,Bontomarannu,Manuju,Parigi,and Tinggimoncong)are ranked as low socio-economic vulnerability.This study can help policy-makers to formulate strategy that contributes to the protection of biodiversity and sustainable development of the Jeneberang watershed,while improving disaster resilience and preparedness of the watershed.

Assessment on the Vulnerability of Agricultural Eco-environment of Hilly Basin Area in the Middle of Hunan Province——A Case Study of Hengyang Basin

[Objective] The study aimed to assess the vulnerability of agricultural eco-environment of hilly basin area in the middle of Hunan Prov- ince. [ Method] Taking Hengyang basin as an example, we firstly chose 13 indicators from natural, social and economic subsystem to assess the vulnerability of agricultural eco-environment in hilly basin area in the middle of Hunan Province. Afterwards, by using principal component analysis, we calculated the weight of each indicator and the vulnerability of agricultural eco-environment of seven regions, finally analyzed the spatial distribu- tion of agricultural eco-envimnment vulnerability. [ Result] The weight of farmers＇ net income per capita was up to 0.140, followed by forest coverage （0.137）. Among the seven counties, the vulnerability of agricultural eco-environment was primarily moderate, while Hengnan County had the highest vulnerability, followed by Hengyang County, and their vulnerability was extremely serious. In addition, Leiyang City was the minimum fragile region, namely slightly fragile region. In general, the vulnerability of peripheral counties was lower than that of central counties. [ Conclusion] The research could provide scientific references for the restoration and reconstruction of agro-ecological environment as well as the establishment of agri- cultural Dreduction decisions.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Understanding livelihood vulnerability:a perspective from Western Sichuan’s ethnic rural settings

To explore the livelihood status and key influencing factors of rural households in the minority areas,we collected flat data from 284 rural households in 32 villages across 12 counties of Western Sichuan from 2021 to 2022.We conducted participatory household survey on the livelihood status of the rural households and try to identify the key factors to influence their livelihood vulnerability using multiple linear regression.The results showed that:the livelihood situation of the rural households is relatively vulnerable.The vulnerability varies significantly with the income levels,education levels,and income sources.The vulnerability of farm households,categorized from low to high livelihood types,follows the sequence:non-agricultural dominant households,non-agricultural households,agricultural dominant households,and pure agricultural households.The degree of damage to the natural environment,education costs,loan opportunities,the proportion of agricultural income to annual household income,and the presence of sick people in the household have significant positive effects on the livelihood vulnerability index(LVI)of rural households;while help from relatives and friends,net income per capita,household size,household education,agricultural land area,participation in industrial organizations,number of livestock,purchase of commercial houses,drinking water source,and self-supply of food have significant negative effects.Based on the findings,we believe that local rural households operate in a complex livelihood system and recommend continuous interventions targeting key influences to provide empirical research support for areas facing similar situations.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

Vulnerability assessment of UAV engine to laser based on improved shotline method

Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Livelihood Vulnerability and Adaptation for Households Engaged in Forestry in Ecological Restoration Areas of the Chinese Loess Plateau

Chinese Loess Plateau has achieved a win-win situation concerning ecological restoration and socio-economic development.However,synergistic development may not be realized at the local scale.In areas undergoing ecological restoration,livelihood vulner-ability may be more pronounced due to the inflexibility,policy protection,and susceptibility to climate and market changes in forestry production.Although this issue has attracted academic interest,empirical studies are relatively scarce.This study,centered on Jiaxian County,Shaanxi Province of China explored the households’livelihood vulnerability and coping strategies and group heterogeneity con-cerned with livelihood structures or forestry resources through field investigation,comprehensive index assessment,and nonparametric tests.Findings showed that:1)the percentage of households with high livelihood vulnerability indicator(LVI)(>0.491)reached 46.34%.2)Eight groups in livelihood structures formed by forestry,traditional agriculture,and non-farm activities were significantly different in LVI,land resources(LR),social networks(SN),livelihood strategies(LS),housing characteristics(HC),and socio-demo-graphic profile(SDP).3)The livelihood vulnerability of the groups with highly engaged/reliance on jujube(Ziziphus jujuba)forest demonstrated more prominent livelihood vulnerability due to the increased precipitation and cold market,where the low-engaged with reliance type were significantly more vulnerable in LVI,SDP,LR,and HC.4)The threshold of behavioral triggers widely varied,and farmers dependent on forestry livelihoods showed negative coping behavior.Specifically,the cutting behavior was strongly associated with lagged years and government subsidies,guidance,and high returns of crops.Finally,the findings can provide guidance on the dir-ection of livelihood vulnerability mitigation and adaptive government management in ecologically restored areas.The issue of farmers’livelihood sustainability in the context of ecological conservation calls for immediate attention,and eco-compensations or other forms of assistance in ecologically functional areas are expected to be enhanced and diversified.

HCRVD: A Vulnerability Detection System Based on CST-PDG Hierarchical Code Representation Learning

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.