Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

Envelope Method and More General New Global Structures of Solutions for Multi-dimensional Conservation Law

For the two-dimensional(2D)scalar conservation law,when the initial data contain two different constant states and the initial discontinuous curve is a general curve,then complex structures of wave interactions will be generated.In this paper,by proposing and investigating the plus envelope,the minus envelope,and the mixed envelope of 2D non-selfsimilar rarefaction wave surfaces,we obtain and the prove the new structures and classifications of interactions between the 2D non-selfsimilar shock wave and the rarefaction wave.For the cases of the plus envelope and the minus envelope,we get and prove the necessary and sufficient criterion to judge these two envelopes and correspondingly get more general new structures of 2D solutions.

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Currently,the cloud computing systems use simple key-value data processing,which cannot support similarity search efectively due to lack of efcient index structures,and with the increase of dimensionality,the existing tree-like index structures could lead to the problem of"the curse of dimensionality".In this paper,a novel VF-CAN indexing scheme is proposed.VF-CAN integrates content addressable network(CAN)based routing protocol and the improved vector approximation fle(VA-fle) index.There are two index levels in this scheme:global index and local index.The local index VAK-fle is built for the data in each storage node.VAK-fle is thek-means clustering result of VA-fle approximation vectors according to their degree of proximity.Each cluster forms a separate local index fle and each fle stores the approximate vectors that are contained in the cluster.The vector of each cluster center is stored in the cluster center information fle of corresponding storage node.In the global index,storage nodes are organized into an overlay network CAN,and in order to reduce the cost of calculation,only clustering information of local index is issued to the entire overlay network through the CAN interface.The experimental results show that VF-CAN reduces the index storage space and improves query performance efectively.

Research and Practice on the Multi-dimensional Course Goal Achievement Evaluation of Software Engineering

According to the standards of engineering education accreditation,the achievement paths and evaluation criteria of course goals are presented,aimed at the objectives of software engineering courses and the characteristics of hybrid teaching in Kunming University of Science and Technology.Then a multi-dimensional evaluation system for course goal achievement of software engineering is proposed.The practice’s results show that the multi-dimensional course goal achievement evaluation is helpful to the continuous improvement of course teaching,which can effectively support the evaluation of graduation outcomes.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Method for evaluation of geological strength index of carbonate cliff rocks:Coupled hyperspectral-digital borehole image technique

The deterioration of unstable rock mass raised interest in evaluating rock mass quality.However,the traditional evaluation method for the geological strength index(GSI)primarily emphasizes the rock structure and characteristics of discontinuities.It ignores the influence of mineral composition and shows a deficiency in assessing the integrity coefficient.In this context,hyperspectral imaging and digital panoramic borehole camera technologies are applied to analyze the mineral content and integrity of rock mass.Based on the carbonate mineral content and fissure area ratio,the strength reduction factor and integrity coefficient are calculated to improve the GSI evaluation method.According to the results of mineral classification and fissure identification,the strength reduction factor and integrity coefficient increase with the depth of rock mass.The rock mass GSI calculated by the improved method is mainly concentrated between 40 and 60,which is close to the calculation results of the traditional method.The GSI error rates obtained by the two methods are mostly less than 10%,indicating the rationality of the hyperspectral-digital borehole image coupled evaluation method.Moreover,the sensitivity of the fissure area ratio(Sr)to GSI is greater than that of the strength reduction factor(a),which means the proposed GSI is suitable for rocks with significant fissure development.The improved method reduces the influence of subjective factors and provides a reliable index for the deterioration evaluation of rock mass.

Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat

In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

Association between triglyceride-glucose index and colorectal polyps:A retrospective cross-sectional study

BACKGROUND Colorectal polyps(CPs)are frequently occurring abnormal growths in the colorectum,and are a primary precursor of colorectal cancer(CRC).The triglyceride-glucose(TyG)index is a novel marker that assesses metabolic health and insulin resistance,and has been linked to gastrointestinal cancers.AIM To investigate the potential association between the TyG index and CPs,as the relation between them has not been documented.METHODS A total of 2537 persons undergoing a routine health physical examination and colonoscopy at The First People's Hospital of Kunshan,Jiangsu Province,China,between January 2020 and December 2022 were included in this retrospective cross-sectional study.After excluding individuals who did not meet the eligibility criteria,descriptive statistics were used to compare characteristics between patients with and without CPs.Logistic regression analyses were conducted to determine the associations between the TyG index and the prevalence of CPs.The TyG index was calculated using the following formula:Ln[triglyceride(mg/dL)×glucose(mg/dL)/2].The presence and types of CPs was determined based on data from colonoscopy reports and pathology reports.RESULTS A nonlinear relation between the TyG index and the prevalence of CPs was identified,and exhibited a curvilinear pattern with a cut-off point of 2.31.A significant association was observed before the turning point,with an odds ratio(95% confidence interval)of 1.70(1.40,2.06),P<0.0001.However,the association between the TyG index and CPs was not significant after the cut-off point,with an odds ratio(95% confidence interval)of 0.57(0.27,1.23),P=0.1521.CONCLUSION Our study revealed a curvilinear association between the TyG index and CPs in Chinese individuals,suggesting its potential utility in developing colonoscopy screening strategies for preventing CRC.

Renal resistive index measurements by ultrasound in patients with liver cirrhosis:Magnitude and associations with renal dysfunction

BACKGROUND The hemodynamic alterations seen in liver cirrhosis lead to renal vasoconstriction,ultimately causing acute kidney injury(AKI).The renal resistive index(RRI)is the most common Doppler ultrasound variable for measuring intrarenal vascular resistance.AIM To evaluate the association of the RRI with AKI in patients with liver cirrhosis and to identify risk factors for high RRI.METHODS This was a prospective observational study,where RRI was measured using Doppler ultrasound in 200 consecutive hospitalized patients with cirrhosis.The association of RRI with AKI was studied.The receiver operating characteristic(ROC)curve analysis was utilized to determine discriminatory cut-offs of RRI for various AKI phenotypes.Multivariate analysis was conducted to determine the predictors of high RRI.RESULTS The mean patient age was 49.08±11.68 years,with the majority(79.5%)being male;the predominant etiology of cirrhosis was alcohol(39%).The mean RRI for the study cohort was 0.68±0.09,showing a progressive increase with higher Child-Pugh class of cirrhosis.Overall,AKI was present in 129(64.5%)patients.The mean RRI was significantly higher in patients with AKI compared to those without it(0.72±0.06 vs 0.60±0.08;P<0.001).A total of 82 patients(41%)had hepatorenal syndrome(HRS)-AKI,29(22.4%)had prerenal AKI(PRA),and 18(13.9%)had acute tubular necrosis(ATN)-AKI.The mean RRI was significantly higher in the ATN-AKI(0.80±0.02)and HRS-AKI(0.73±0.03)groups than in the PRA(0.63±0.07)and non-AKI(0.60±0.07)groups.RRI demonstrated excellent discriminatory ability in distinguishing ATN-AKI from non-ATN-AKI(area under ROC curve:93.9%).AKI emerged as an independent predictor of high RRI(adjusted odds ratio[OR]:11.52),and high RRI independently predicted mortality among AKI patients(adjusted OR:3.18).CONCLUSION In cirrhosis patients,RRI exhibited a significant association with AKI,effectively differentiated between AKI phenotypes,and predicted AKI mortality.

Three-dimensional choroidal vascularity index and choroidal thickness in fellow eyes of acute and chronic primary angle-closure using swept-source optical coherence tomography

AIM:To compare the three-dimensional choroidal vascularity index(CVI)and choroidal thickness between fellow eyes of acute primary angle-closure(F-APAC)and chronic primary angle-closure glaucoma(F-CPACG)and the eyes of normal controls.METHODS:This study included 37 patients with unilateral APAC,37 with asymmetric CPACG without prior treatment,and 36 healthy participants.Using swept-source optical coherence tomography(SS-OCT),the macular and peripapillary choroidal thickness and three-dimensional CVI were measured and compared globally and sectorally.Pearson’s correlation analysis and multivariate regression models were used to evaluate choroidal thickness or CVI with related factors.RESULTS:The mean subfoveal CVIs were 0.35±0.10,0.33±0.09,and 0.29±0.04,and the mean subfoveal choroidal thickness were 315.62±52.92,306.22±59.29,and 262.69±45.55μm in the F-APAC,F-CPACG,and normal groups,respectively.All macular sectors showed significantly higher CVIs and choroidal thickness in the F-APAC and F-CPACG eyes than in the normal eyes(P<0.05),while there were no significant differences between the F-APAC and F-CPACG eyes.In the peripapillary region,the mean overall CVIs were 0.21±0.08,0.20±0.08,and 0.19±0.05,and the mean overall choroidal thickness were 180.45±54.18,174.82±50.67,and 176.18±37.94μm in the F-APAC,F-CPACG,and normal groups,respectively.There were no significant differences between any of the two groups in all peripapillary sectors.Younger age,shorter axial length,and the F-APAC or F-CPACG diagnosis were significantly associated with higher subfoveal CVI and thicker subfoveal choroidal thickness(P<0.05).CONCLUSION:The fellow eyes of unilateral APAC or asymmetric CPACG have higher macular CVI and choroidal thickness than those of the normal controls.Neither CVI nor choroidal thickness can distinguish between eyes predisposed to APAC or CPACG.A thicker choroid with a higher vascular volume may play a role in the pathogenesis of primary angle-closure glaucoma.

On traceable iterated line graph and hamiltonian path index

Xiong and Liu[21]gave a characterization of the graphs G for which the n-iterated line graph L^(n)(G)is hamiltonian,for n≥2.In this paper,we study the existence of a hamiltonian path in L^(n)(G),and give a characterization of G for which L^(n)(G)has a hamiltonian path.As applications,we use this characterization to give several upper bounds on the hamiltonian path index of a graph.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Association between the cumulative triglyceride-glucose index and the recurrence of atrial fibrillation after radiofrequency catheter ablation

BACKGROUND Triglyceride-glucose(TyG)index values are a new surrogate marker for insulin resistance.This study aimed to explore the relationship between cumulative TyG index values and atrial fibrillation(AF)recurrence after radiofrequency catheter ablation(RFCA).METHODS A total of 576 patients with AF who underwent RFCA at the Second Affiliated Hospital of Xi'an Jiaotong University were included in this study.The participants were grouped based on cumulative TyG index values tertiles within 3 months after ablation.Cox regression and restricted cubic spline analyses were used to determine the relationship between cumulative TyG index values and AF recurrence.The predictive value of all risk factors was assessed by receiver operating curve analysis.RESULTS There were 375 patients completed the study(age:63.23±10.73 years,64.27%male).The risk of AF recurrence increased with increasing cumulative TyG index values tertiles.After adjusting for potential confounders,patients in the medium cumulative TyG index group[hazard ratio(HR)=4.949,95%CI:1.778–13.778,P=0.002]and the high cumulative TyG index group(HR=8.716,95%CI:3.371–22.536,P<0.001)had a higher risk of AF recurrence than those in the low cumulative TyG index group.The restricted cubic spline regression model also showed an increased risk of AF recurrence with increasing cumulative TyG index values.When considering cumulative TyG index values,left atrial diameter,and lactate dehydrogenase levels as a comprehensive factor,the model could effectively predict AF recurrence after RFCA[area under the curve(AUC)=0.847,95%CI:0.797–0.897,P<0.001].CONCLUSIONS Cumulative TyG index values were a risk factor for AF recurrence after RFCA.Monitoring longitudinal TyG index values may assist with optimized for risk stratification and outcome prediction for AF recurrence.

An evaluation of ureteral diameter ratio and vesicoureteral reflux index in the treatment of primary vesicoureteral reflux

Objective:Vesicoureteral reflux(VUR)index is a simple,validated tool that reliably predicts significant improvement and spontaneous resolution of primary reflux in children.The aim of this study was to evaluate and compare the ureter diameter ratio(UDR)and VUR index(VURx)of patients treated with endoscopic injection(EI)and ureteroneocystostomy(UNC)methods in the pediatric age group due to primary VUR.Methods:Patients under the age of 18 years old who underwent EI and UNC with the diagnosis of primary VUR between January 2011 and September 2021 were determined as the participants.The UDR was assessed using voiding cystourethrography,and the VURx score was determined prior to treatment based on hospital records included in the study.Results:A total of 255 patients,60(23.5%)boys and 195(76.5%)girls,with a mean age of 76.5(range 13.0e204.0)months,were included in the study.EI was applied to 130(51.0%)patients and UNC was applied to 125(49.0%)patients due to primary VUR.The optimum cut-off for the distal UDR was obtained as 0.17 with sensitivity and specificity of 73.0%and 63.0%,respectively.The positive and negative predictive values were 66.0%and 70.0%,respectively.Conclusion:When the UDR and VURx score are evaluated together for the surgical treatment of primary VUR in the pediatric age group,it is thought that it may be useful in predicting the clinical course of the disease and evaluating surgical treatment options.

Assessment on eco-environmental quality of the Yellow River Basin by considering desertification index

Desertification has had a significant impact on the ecological environment of the Yellow River Basin(YRB)in China.However,previous studies on the evaluation of the ecological environment quality(EEQ)in the YRB have paid limited attention to the indicator of desertification.It is of great significance to incorporate the desertification index into the spatiotemporal assessment of the EEQ in the YRB in order to protect the ecological environment in the region.In this study,based on multi-source remote sensing data from 91 cities in the YRB,this article proposes a desertification remote sensing ecological index(DRSEI)model,which builds upon the traditional Remote Sensing Ecological Index(RSEI)model,to analyze the spatiotemporal changes in the EEQ in the YRB from 2001 to 2021.Furthermore,using the geographic detector(GD),and geographically and temporally weighted regression(GTWR)model,the study assesses the impact of human and natural factors on the EEQ in the YRB.The research findings indicate that:(1)Compared to the traditional RSEI,the improved DRSEI shows a decreasing trend in the evaluation results of the EEQ.Among the 24 cities,the change in DRSEI exceeds 0.05 compared to RSEI,accounting for 26.37%of the YRB.The remaining 67 cities have changes within a range of less than 0.05,accounting for 73.63%of the YRB.(2)The results of the GD for individual and interactive effects reveal that rainfall and elevation have significant individual and interactive effects on the EEQ.Furthermore,after the interaction with natural factors,the explanatory power of human factors gradually increases over time.The spatial heterogeneity results of GTWR demonstrate that rainfall has a strong direct positive impact on the EEQ,accounting for 98.90%of the influence,while temperature exhibits a more pronounced direct inhibitory effect,accounting for 76.92%of the influence.Human activities have a strong negative impact on the EEQ and a weak positive impact.