A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Exercised blood plasma promotes hippocampal neurogenesis in the Alzheimer's disease rat brain

Background:Exercise training promotes brain plasticity and is associated with protection against cognitive impairment and Alzheimer’s disease(AD).These beneficial effects may be partly mediated by blood-borne factors.Here we used an in vitro model of AD to investigate effects of blood plasma from exercise-trained donors on neuronal viability,and an in vivo rat model of AD to test whether such plasma impacts cognitive function,amyloid pathology,and neurogenesis.Methods:Mouse hippocampal neuronal cells were exposed to AD-like stress using amyloid-βand treated with plasma collected from human male donors 3 h after a single bout of high-intensity exercise.For in vivo studies,blood was collected from exercise-trained young male Wistar rats(high-intensity intervals 5 days/week for 6 weeks).Transgenic AD rats(McGill-R-Thyl-APP)were inj ected 5 times/fortnight for 6 weeks at2 months or 5 months of age with either(a)plasma from the exercise-trained rats,(b)plasma from sedentary rats,or(c)saline.Cognitive function,amyloid plaque pathology,and neurogenesis were assessed.The plasma used for the treatment was analyzed for 23 cytokines.Results:Plasma from exercised donors enhanced cell viability by 44.1%(p=0.032)and reduced atrophy by 50.0%(p<0.001)in amyloid-β-treated cells.In vivo exercised plasma treatment did not alter cognitive function or amyloid plaque pathology but did increase hippocampal neurogenesis by~3 fold,regardless of pathological stage,when compared to saline-treated rats.Concentrations of 7 cytokines were significantly reduced in exercised plasma compared to sedentary plasma.Conclusion:Our proof-of-concept study demonstrates that plasma from exercise-trained donors can protect neuronal cells in culture and promote adult hippocampal neurogenesis in the AD rat brain.This effect may be partly due to reduced pro-inflammatory signaling molecules in exercised plasma.

Effect of the interval between two shocks on ejecta formation from the grooved aluminum surface

This work focuses on the effect of the interval between two shocks on the ejecta formation from the grooved aluminum(Al_(1100))surface by using smoothed particle hydrodynamics numerical simulation.Two unsupported shocks are obtained by the plate-impact between sample and two flyers at interval,with a peak pressure of approximately 30 GPa for each shock.When the shock interval varies from 2.11 to 7.67 times the groove depth,the bubble velocity reduces to a constant,and the micro jetting factor R_(J) from spike to bubble exhibits a non-monotonic change that decreases initially and then increases.At a shock interval of 3.6 times the groove depth,micro jetting factor R_(J) from spike to bubble reaches its minimum value of approximately 0.6.While,the micro jetting factor R_(F) from spike to free surface decreases linearly at first,and stabilizes around 0.25 once the shock interval surpasses 4.18 times the groove depth.When the shock interval is less than 4.18 times the groove depth,the unloading wave generated by the breakout of the first shock wave is superimpose with the unloading part of the second shock wave to form a large tensile area.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Suppressive effects of exercise-conditioned serum on cancer cells:A narrative review of the influence of exercise mode,volume,and intensity

Cancer is a major cause of morbidity and mortality worldwide,and the incidence is increasing,highlighting the need for effective strategies to treat this disease.Exercise has emerged as fundamental therapeutic medicine in the management of cancer,associated with a lower risk of recur-rence and increased survival.Several avenues of research demonstrate reduction in growth,proliferation,and increased apoptosis of cancer cells,including breast,prostate,colorectal,and lung cancer,when cultured by serum collected after exercise in vitro(i.e.,the cultivation of cancer cell lines in an experimental setting,which simplifies the biological system and provides mechanistic insight into cell responses).The underlying mechanisms of exercise-induced cancer suppressive effects may be attributed to the alteration in circulating factors,such as skeletal muscle-induced cytokines(i.e.,myokines)and hormones.However,exercise-induced tumor suppressive effects and detailed information about training interventions are not well investigated,constraining more precise application of exercise medicine within clinical oncology.To date,it remains unclear what role different training modes(i.e.,resistance and aerobic training)as well as volume and intensity have on exercise-condi-tioned serum and its effects on cancer cells.Nevertheless,the available evidence is that a single bout of aerobic training at moderate to vigorous intensity has cancer suppressive effects,while for chronic training interventions,exercise volume appears to be an influential candidate driving cancer inhibitory effects regardless of training mode.Insights for future research investigating training modes,volume and intensity are provided to further our understanding of the effects of exercise-conditioned serum on cancer cells.

Alternative Method of Constructing Granular Neural Networks

Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.

Comparison of QT Correction Methods in the Pediatric Population of a Community Hospital: A Retrospective Study

Objective:Accurate measurement of QT interval,the ventricular action potential from depolarization to repolarization,is important for the early detection of Long QT syndrome.The most effective QT correction(QTc)formula has yet to be determined in the pediatric population,although it has intrinsically greater extremes in heart rate(HR)and is more susceptible to errors in measurement.The authors of this study compare six dif-ferent QTc methods(Bazett,Fridericia,Framingham,Hodges,Rautaharju,and a computer algorithm utilizing the Bazett formula)for consistency against variations in HR and RR interval.Methods:Descriptive Retrospective Study.We included participants from a pediatric cardiology practice of a community hospital who had an ECG performed in 2017.All participants were healthy patients with no past medical history and no regular med-ications.Results:ECGs from 95 participants from one month to 21 years of age(mean 9.7 years)were included with a mean HR of 91 beats per minute(bpm).The two-sample paired t-test or Wilcoxon signed-rank test assessed for any difference between QTc methods.A statistically significant difference was observed between every combination of two QTc formulae.The Spearman’s rank correlation analysis explored the QTc/HR and QTc/RR relationships for each formula.Fridericia method was most independent of HR and RR with the lowest absolute value of correlation coefficients.Bazett and Computer had moderate correlations,while Framingham and Rautaharju exhibited strong correlations.Correlations were positive for Bazett and Computer,reflecting results from prior studies demonstrating an over-correction of Bazett at higher HRs.In the linear QTc/HR regression analysis,Bazett had the slope closest to zero,although Computer,Hodges,and Fridericia had comparable values.Alternatively,Fridericia had the linear QTc/RR regression coefficient closest to zero.The Bland-Altman method assessed for bias and the limits of agreement between correction formulae.Bazett and Computer exhibited good agreement with minimal bias along with Framingham and Rautaharju.To account for a possible skewed distri-bution of QT,all the above analyses were also performed excluding the top and bottom 2%of data as sorted by heart rate ranges(N=90).Results from this data set were consistent with those derived from all participants(N=95).Conclusions:Overall,the Fridericia correction method provided the best rate correction in our pedia-tric study cohort.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

Rice University Rule to Determine the Number of Bins

This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.

Precision in detecting colon lesions:A key to effective screening policy but will it improve overall outcomes?

Colonoscopy is the gold standard for the screening and diagnosis of colorectal cancer,resulting in a decrease in the incidence and mortality of colon cancer.However,it has a 21%rate of missed polyps.Several strategies have been devised to increase polyp detection rates and improve their characterization and delimi-tation.These include chromoendoscopy(CE),the use of other devices such as Endo cuffs,and major advances in endoscopic equipment[high definition,magnification,narrow band imaging,i-scan,flexible spectral imaging color enhancement,texture and color enhancement imaging(TXI),etc.].In the retrospective study by Hiramatsu et al,they compared white-light imaging with CE,TXI,and CE+TXI to determine which of these strategies allows for better definition and delimitation of polyps.They concluded that employing CE associated with TXI stands out as the most effective method to utilize.It remains to be demonstrated whether these results are extrapolatable to other types of virtual CE.Additionally,further investigation is needed in order to ascertain whether this strategy could lead to a reduction in the recurrence of excised lesions and potentially lower the occurrence of interval cancer.

Near-Field Beam Training for Holographic MIMO Communications: Typical Methods, Challenges and Future Directions

Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.

Enrichment factors of movable hydrocarbons in lacustrine shale oil and exploration potential of shale oil in Gulong Sag,Songliao Basin,NE China

The geological characteristics and production practices of the major middle-and high-maturity shale oil exploration areas in China are analyzed.Combined with laboratory results,it is clear that three essential conditions,i.e.economic initial production,commercial cumulative oil production of single well,and large-scale recoverable reserves confirmed by the testing production,determine whether the continental shale oil can be put into large-scale commercial development.The quantity and quality of movable hydrocarbons are confirmed to be crucial to economic development of shale oil,and focuses in evaluation of shale oil enrichment area/interval.The evaluation indexes of movable hydrocarbon enrichment include:(1)the material basis for forming retained hydrocarbon,including TOC>2%(preferentially 3%-4%),and typeⅠ-Ⅱkerogens;(2)the mobility of retained hydrocarbon,which is closely related to the hydrocarbon composition and flow behaviors of light/heavy components,and can be evaluated from the perspectives of thermal maturity(Ro),gas-oil ratio(GOR),crude oil density,quality of hydrocarbon components,preservation conditions;and(3)the reservoir characteristics associated with the engineering reconstruction,including the main pore throat distribution zone,reservoir physical properties(including fractures),lamellation feature and diagenetic stage,etc.Accordingly,13 evaluation indexes in three categories and their reference values are established.The evaluation indicates that the light shale oil zones in the Gulong Sag of Songliao Basin have the most favorable enrichment conditions of movable hydrocarbons,followed by light oil and black oil zones,containing 20.8×10^(8) t light oil resources in reservoirs with R_(0)>1.2%,pressure coefficient greater than 1.4,effective porosity greater than 6%,crude oil density less than 0.82 g/cm^(3),and GOR>100 m/m^(3).The shale oil in the Gulong Sag can be explored and developed separately by the categories(resource sweet spot,engineering sweet spot,and tight oil sweet spot)depending on shale oil flowability.The Gulong Sag is the most promising area to achieve large-scale breakthrough and production of continental shale oil in China.

PRI modulation recognition and sequence search under small sample prerequisite

Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide radars are highly confusing.There are few available training samples in practical situations,which leads to a low recognition accuracy and poor search effect of the pulse sequence.In this paper,an approach based on bi-directional long short-term memory(BiLSTM)networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed.The simulation results demonstrate that the proposed algorithm can recognize unilinear,bilinear,sawtooth,and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.

Rapid diagnostic method for transplutonium isotope production in high flux reactors

Transplutonium isotopes are scarce and need to be produced by irradiation in high flux reactors.However,their production is inefficient,and optimization studies are necessary.This study analyzes the physical nature of transplutonium isotope produc-tion using ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu as examples.Traditional methods based on the Monte Carlo burnup calculation have the limitations of many calculations and cannot analyze the individual energy intervals in detail;thus,they cannot sup-port the refined evaluation,screening,and optimization of the irradiation schemes.After understanding the physical nature and simplifying the complexity of the production process,we propose a rapid diagnostic method for evaluating radiation schemes based on the concepts“single energy interval value(SEIV)”and“energy spectrum total value(ESTV)”.The rapid diagnostic method not only avoids tedious burnup calculations,but also provides a direction for optimization.The optimal irradiation schemes for producing ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu are determined based on a rapid diagnostic method.Optimal irradiation schemes can significantly improve production efficiency.Compared with the initial scheme,the optimal scheme improved the production efficiency of ^(238)Pu by 7.41 times;^(242)Cm,11.98 times;^(244)Cm,65.20 times;and ^(252)Cf,15.08 times.Thus,a refined analysis of transplutonium isotope production is conducted and provides a theoretical basis for improving production efficiency.

Recognition of dynamically varying PRI modulation via deep learning and recurrence plot

Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.

Flexural wave bandgap properties of phononic crystal beams with interval parameters

Uncertainties are unavoidable in practical engineering,and phononic crystals are no exception.In this paper,the uncertainties are treated as the interval parameters,and an interval phononic crystal beam model is established.A perturbation-based interval finite element method(P-IFEM)and an affine-based interval finite element method(A-IFEM)are proposed to study the dynamic response of this interval phononic crystal beam,based on which an interval vibration transmission analysis can be easily implemented and the safe bandgap can be defined.Finally,two numerical examples are investigated to demonstrate the effectiveness and accuracy of the P-IFEM and A-IFEM.Results show that the safe bandgap range may even decrease by 10%compared with the deterministic bandgap without considering the uncertainties.

Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-based Contracting

With the further development of service-oriented,performance-based contracting(PBC)has been widely adopted in industry and manufacturing.However,maintenance optimization problems under PBC have not received enough attention.To further extend the scope of PBC’s application in the field of maintenance optimization,we investigate the condition-based maintenance(CBM)optimization for gamma deteriorating systems under PBC.Considering the repairable single-component system subject to the gamma degradation process,this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC.In the proposed CBM model,the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate.Then,a particle swarm algorithm(PSO)and related solution procedure are presented to solve the multiple decision variables in our proposed model.In the end,a numerical example is provided so as to demonstrate the superiority of the presented model.By comparing the proposed policy with the conventional ones,the superiority of our proposed policy is proved,which can bring more profits to providers and improve performance.Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy.A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems:A Case Study in Level Control Process

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work.Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants.Also,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)controller.To the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time delay.The three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction.Finally,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

A new three-parameter discrete distribution called the zero-inflated cosine geometric(ZICG)distribution is proposed for the first time herein.It can be used to analyze over-dispersed count data with excess zeros.The basic statistical properties of the new distribution,such as the moment generating function,mean,and variance are presented.Furthermore,confidence intervals are constructed by using the Wald,Bayesian,and highest posterior density(HPD)methods to estimate the true confidence intervals for the parameters of the ZICG distribution.Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the Olympic Games in Tokyo 2020.The results show that the HPD interval performed better than the other methods in terms of coverage probability and average length in most cases studied.

Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China

In the context of global warming,drought events occur frequently.In order to better understanding the process and mechanism of drought occurrence and evolution,scholars have dedicated much attention on drought propagation,mainly focusing on drought propagation time and propagation probability.However,there are relatively few studies on the sensitivities of drought propagation to seasons and drought levels.Therefore,we took the Heihe River Basin(HRB)of Northwest China as the case study area to quantify the propagation time and propagation probability from meteorological drought to agricultural drought during the period of 1981–2020,and subsequently explore their sensitivities to seasons(irrigation and non-irrigation seasons)and drought levels.The correlation coefficient method and Copula-based interval conditional probability model were employed to determine the drought propagation time and propagation probability.The results determined the average drought propagation time as 8 months in the whole basin,which was reduced by 2 months(i.e.,6 months)on average during the irrigation season and prolonged by 2 months(i.e.,10 months)during the non-irrigation season.Propagation probability was sensitive to both seasons and drought levels,and the sensitivities had noticeable spatial differences in the whole basin.The propagation probability of agricultural drought at different levels generally increased with the meteorological drought levels for the upstream,midstream,and southern downstream regions of the HRB.Lesser agricultural droughts were more likely to be triggered during the irrigation season,while severer agricultural droughts were occurred mostly during the non-irrigation season.The research results are helpful to understand the characteristics of drought propagation and provide a scientific basis for the prevention and control of droughts.This study is of great significance for the rational planning of local water resources and maintaining good ecological environment in the HRB.