Most research on carbon storage in forests has focused on qualitative studies of carbon storage and influ-encing factors rather than on quantifying the effect of the spatial distribution of carbon storage and of its i...Most research on carbon storage in forests has focused on qualitative studies of carbon storage and influ-encing factors rather than on quantifying the effect of the spatial distribution of carbon storage and of its influencing factors at different scales.Here we described the spatial dis-tribution of aboveground carbon storage(ACS)in a 20-ha plot in a subtropical evergreen broad-leaved forest to evalu-ate and quantify the relative effects of biotic factors(species diversity and structural diversity)and abiotic factors(soil and topographic factors)on ACS at different scales.Scale effects of the spatial distribution of ACS were significant,with higher variability at smaller scales,but less at larger scales.The distribution was also spatially heterogeneous,with more carbon storage on north-and east-facing slopes than on south-and west-facing slopes.At a smaller scale,species diversity and structural diversity each had a direct positive impact on ACS,but soil factors had no significant direct impact.At increasing scales,topographic and soil fac-tors gradually had a greater direct influence,whereas the influence of species diversity gradually decreased.Structural diversity had the greatest impact,followed by topographic factors and soil factors,while species diversity had a rela-tively smaller impact.These findings suggest studies on ACS in subtropical evergreen broadleaf forests in southern China should consider scale effects,specifically on the heterogene-ity of ACS distribution at small scales.Studies and conser-vation efforts need to focus on smaller habitat types with particular emphasis on habitat factors such as aspect and soil conditions,which have significant influences on community species diversity,structural diversity,and ACS distribution.展开更多
The topographic factor(LS factor),derived from the multiplication of the slope length(L)and slope steepness(S)factors,is a vital parameter in soil erosion models.Generated from the digital elevation model(DEM),the LS ...The topographic factor(LS factor),derived from the multiplication of the slope length(L)and slope steepness(S)factors,is a vital parameter in soil erosion models.Generated from the digital elevation model(DEM),the LS factor always varies with the changing DEM resolution,i.e.,the LS factor scale effect.Previous studies have found the phenomenon of the LS factor scale effect,but the underlying causes of this phenomenon has not been well explored.Therefore,how the DEM resolution affects the LS factor and how the scale effect of the L and S factors influence the LS factor scale effect remains unclear.To address these problems,we collected 20 watersheds from the Guangdong Province with different topographic reliefs,and compared the corresponding L,S and LS factors at 10-m and 30-m resolution DEMs.Our results indicate that the S factor,heavily influenced by slope underestimation in coarse-resolution DEMs,makes a difference in the LS factor scale effect.In addition,the LS factor scale effect becomes less significant with increasing reliefs,suggesting the possibility of using 30-m DEM for LS calculation in rugged terrains.Our findings on the underlying mechanisms of the LS factor scale effect help to identify the uncertainty in the LS factor estimation,thereby enhancing the accuracy of soil erosion assessment,particularly in regions with different topographic characteristics and contribute to more effective soil conservation strategies and decision-making.展开更多
With the continuous evolution of urban surface types,the impact of the urban heat island effect on the human population has intensified.Investigating the factors influencing urban thermal environments is crucial for p...With the continuous evolution of urban surface types,the impact of the urban heat island effect on the human population has intensified.Investigating the factors influencing urban thermal environments is crucial for providing theoretical support to urban planning and decision-making.In this study,Shenyang was selected to comprehensively analyse multiple factors,including topography,human activity,vegetation and landscape.Moreover,we used the random forest algorithm to explore nonlinear factors influencing land surface temperature(LST)over four years in the study area.The results revealed that from 2005 to 2020,the total areas with sub-high and high-temperature zones in northern Shenyang steadily increased.The area ratio of these zones increased from 20.18% in 2005 to 24.86% in 2020.Additionally,significant and strong correlations were observed between LST and variables such as the enhanced vegetation index(EVI),normalised difference vegetation index(NDVI),population density,proportion of cropland and proportion of impervious land.In 2010,proportion of impervious land exhibited the strongest correlation with LST at the 5 km scale,reaching 0.852(p<0.01).The 4 km grid scale was identified as the optimal grid size for this study,while the 2 km grid performed the worst.In 2020,NDVI emerged as the most significant factor influencing LST.These findings provide valuable guidance for improving urban planning and developing sustainable strategies.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electro...Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.展开更多
Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the chall...Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.展开更多
BACKGROUND Antenatal depression is a disabling mental disorder among pregnant women and may cause adverse outcomes for both the mother and the offspring.Early identification and intervention of antenatal depression ca...BACKGROUND Antenatal depression is a disabling mental disorder among pregnant women and may cause adverse outcomes for both the mother and the offspring.Early identification and intervention of antenatal depression can help to prevent adverse outcomes.However,there have been few population-based studies focusing on the association of social and obstetric risk factors with antenatal depression in China.AIM To assess the sociodemographic and obstetric factors of antenatal depression and compare the network structure of depressive symptoms across different risk levels based on a large Chinese population.METHODS The cross-sectional survey was conducted in Shenzhen,China from 2020 to 2024.Antenatal depression was assessed using the Chinese version of the Edinburgh Postnatal Depression Scale(EPDS),with a score of≥13 indicating the presence of probable antenatal depression.Theχ2 test and binary logistic regression were used to identify the factors associated with antenatal depression.Network analyses were conducted to investigate the structure of depressive symptoms across groups with different risk levels.RESULTS Among the 44220 pregnant women,the prevalence of probable antenatal depression was 4.4%.An age≤24 years,a lower level of education(≤12 years),low or moderate economic status,having a history of mental disorders,being in the first trimester,being a primipara,unplanned pregnancy,and pregnancy without pre-pregnancy screening were found to be associated with antenatal depression(all P<0.05).Depressive symptom networks across groups with different risk levels revealed robust interconnections between symptoms.EPDS8("sad or miserable")and EPDS4("anxious or worried")showed the highest nodal strength across groups with different risk levels.CONCLUSION This study suggested that the prevalence of antenatal depression was 4.4%.Several social and obstetric factors were identified as risk factors for antenatal depression.EPDS8("sad or miserable")and EPDS4("anxious or worried")are pivotal targets for clinical intervention to alleviate the burden of antenatal depression.Early identification of highrisk groups is crucial for the development and implementation of intervention strategies to improve the overall quality of life for pregnant women.展开更多
To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorit...To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorithm is proposed. Based on the relationship between the iteration formulae, a new iteration formula is introduced, which leads the correction operation to be several simple shifting and adding operations. As one key part, the effects caused by rounding error are analyzed mathematically and it is concluded that the effects can be degraded by an appropriate selection of coefficients in the iteration formula. The model is then set up in Matlab and coded in Verilog HDL language. The proposed algorithm is also synthesized and verified in field-programmable gate array （FPGA）. The results show that this new scheme requires only one additional clock cycle and there is no change in the elementary iteration for the same precision compared with the conventional algorithm. In addition, the circuit realization is regular and the change in system throughput is very minimal.展开更多
The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by diff...The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.展开更多
In order to analyze the effect of wavelength-dependent radiation-induced attenuation （RIA） on the mean trans- mission wavelength in optical fiber and the scale factor of interferometric fiber optic gyroscopes （IFOG...In order to analyze the effect of wavelength-dependent radiation-induced attenuation （RIA） on the mean trans- mission wavelength in optical fiber and the scale factor of interferometric fiber optic gyroscopes （IFOGs）, three types of polarization-maintaining （PM） fibers are tested by using a 60Co γ-radiation source. The observed different mean wave- length shift （MWS） behaviors for different fibers are interpreted by color-center theory involving dose rate-dependent absorption bands in ultraviolet and visible ranges and total dose-dependent near-infrared absorption bands. To evaluate the mean wavelength variation in a fiber coil and the induced scale factor change for space-borne IFOGs under low radiation doses in a space environment, the influence of dose rate on the mean wavelength is investigated by testing four germanium （Ge） doped fibers and two germanium-phosphorus （Ge-P） codoped fibers irradiated at different dose rates. Experimental results indicate that the Ge-doped fibers show the least mean wavelength shift during irradiation and their mean wavelength of optical signal transmission in fibers will shift to a shorter wavelength in a low-dose-rate radiation environment. Finally, the change in the scale factor of IFOG resulting from the mean wavelength shift is estimated and tested, and it is found that the significant radiation-induced scale factor variation must be considered during the design of space-borne IFOGs.展开更多
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East China.T...This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East China.The scale-dependent error growth(ensemble variability)and associated impact on precipitation forecasts(precipitation uncertainties)were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing.The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing.This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales.The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale,suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties,especially for the strong-forcing regime.Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors.Specifically,small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing.Meanwhile,larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale.Consequently,these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.展开更多
Multifdelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from diferent fdelity data with a signifcant improvement in modeling efciency.In this paper,a modifed MFS(...Multifdelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from diferent fdelity data with a signifcant improvement in modeling efciency.In this paper,a modifed MFS(MMFS)model based on a radial basis function(RBF)is proposed,in which two fdelities of information can be analyzed by adaptively obtaining the scale factor.In the MMFS,an RBF was employed to establish the low-fdelity model.The correlation matrix of the high-fdelity samples and corresponding low-fdelity responses were integrated into an expansion matrix to determine the scaling function parameters.The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fdelity sample points.The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF)and single-fdelity RBF using four benchmark test functions,by which the impacts of diferent high-fdelity sample sizes on the prediction accuracy were also analyzed.The sensitivity of the MMFS model to the randomness of the design of experiments(DoE)was investigated by repeating sampling plans with 20 diferent DoEs.Stress analysis of the steel plate is presented to highlight the prediction ability of the proposed MMFS model.This research proposes a new multifdelity modeling method that can fully use two fdelity sample sets,rapidly calculate model parameters,and exhibit good prediction accuracy and robustness.展开更多
This paper explains that the terms“horizontal and vertical scales”are not appropriate in map projections theory.Instead,the authors suggest using the term“scales in the direction of coordinate axes.”Since it is no...This paper explains that the terms“horizontal and vertical scales”are not appropriate in map projections theory.Instead,the authors suggest using the term“scales in the direction of coordinate axes.”Since it is not possible to read a local linear scale factor in the direction of a coordinate axis immediately from the definition of a local linear scale factor,this paper considers the derivation of new formulae that enable local linear scale factors in the direction of coordinate x and y axes to be calculated.The formula for computing the local linear scale factor in any direction defined by dx and dy is also derived.Furthermore,the position and magnitude of the extreme values of the local linear scale factor are considered and new formulas derived.展开更多
Four ships,a twin-propeller naval ship,two single-propeller container ships,and a single-propeller very large crude carrier(VLCC),were studied to investigate the scale effect of the form factor.The viscous flow fields...Four ships,a twin-propeller naval ship,two single-propeller container ships,and a single-propeller very large crude carrier(VLCC),were studied to investigate the scale effect of the form factor.The viscous flow fields of the ships at different scales were solved numerically via the Reynolds-averaged Navier–Stokes method combined with the shear stress transport k–ωturbulence model.The numerical method was validated through comparisons with experimental data,and numerical uncertainty analysis was carried out based on the ITTC recommended procedure.On this basis,scale effects of the form factor were analyzed using different friction lines,and scale effects of flow fields and the mean axial wake fractions were further analyzed in details.The results showed that the form factor exhibited scale effects when adopting the ITTC-1957 line,and it increased with the increase in the Reynolds number.The scale effect of the form factor reduces the prediction precision of the full-scale ship resistance.The friction line has a significant effect on the form factor.The form factor exhibits little dependence on the Reynolds number when using the numerical friction line or the Katsui line,which is useful for full-scale ship resistance predictions.With the increasing Reynolds number,the boundary layer thickness becomes thinner and the axial velocity contour contracts toward the center plane,and there is nearly a linear relationship between the reciprocal of mean axial wake fraction on propeller disc and Reynolds number in logarithmic scale for the three types of ship forms.展开更多
This paper, divided into three parts (Part II-A, Part II-B and Part II-C), contains the detailed factorizational theory of asymptotic expansions of type (?)?, , , where the asymptotic scale?, , is assumed to be an ext...This paper, divided into three parts (Part II-A, Part II-B and Part II-C), contains the detailed factorizational theory of asymptotic expansions of type (?)?, , , where the asymptotic scale?, , is assumed to be an extended complete Chebyshev system on a one-sided neighborhood of . It follows two pre-viously published papers: the first, labelled as Part I, contains the complete (elementary but non-trivial) theory for;the second is a survey highlighting only the main results without proofs. All the material appearing in §2 of the survey is here reproduced in an expanded form, as it contains all the preliminary formulas necessary to understand and prove the results. The remaining part of the survey—especially the heuristical considerations and consequent conjectures in §3—may serve as a good introduction to the complete theory.展开更多
In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances ...In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The varia- tions of analysis increments with different-scale errors are consistent with those of variances and correlations of back- ground errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Tem- perature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more ac- curate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- （smaller-） scale errors will be beneficial for the accuracy of forecasts of heavy （light） precipitation at large （small） scales because of the ampli- fication （diminution） of the intensity and area in precipitation forecasts.展开更多
Based on the data of 30 Chinese provinces for the period from 2004 to 2015,this paper expounds the carbon emissions effect of two-way foreign direct investment (FDI) from the perspective of scale effect and factor mar...Based on the data of 30 Chinese provinces for the period from 2004 to 2015,this paper expounds the carbon emissions effect of two-way foreign direct investment (FDI) from the perspective of scale effect and factor market distortions.This study uses Kaya identity to decompose carbon emission and construct simultaneous equations model to empirically examine the factor market distortion and the carbon emission scale effect of two-way FDI.The results show that the inward foreign direct investment (IFDI) increase regional carbon emission through scale effect and also exacerbates factor market distortion in China,whereas the outward FDI trends reduce carbon emission and reduces factor market distortions in China.The study also shows that human capital,research and development (R&D),trade openness,and capital accumulation are important determinants of two-way FDI.Therefore,the study proposes that IFDI policies should focus on acquiring green technologies.In addition,the domestic enterprises should be encouraged to participate in global business.展开更多
The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advan...The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.展开更多
The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representat...The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results.It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values.To explore the impacts of the generalization scales,we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization(CAPSO)and applied them to simulate urban growth of Suzhou during 2000-2020.Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance(ERD)of the Generalized Additive Models(GAMs).Specifically,the ERD declined from 51.9%to 45.9%as the factor scale became larger during 2000-2020,but there was a peak value(52.2%)at Scale-2.For all simulations during 2000-2020,the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit(FOM),which respectively decreased by 3.1%and 4.4%as compared to the CA models with scale-free factors.We concluded that the driving factors at a smaller scale(200~400 m for point-like facilities and 7~14 m for line-like facilities)can build more accurate CA models to simulate urban growth patterns,and the optimal scale for factors can be identified using the ERD.This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.展开更多
In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivit...In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivity field of a heterogeneous aquifer based on synthetic experiments.By increasing the numbers of observation wells and pumping tests,we analyzed the difference between the estimated and true values of hydraulic conductivity with different correlation scale errors.The relationships between the observation well number and the error in inversion results,and between the pumping test number and the error in inversion results were investigated.The results show that,if the amount of observed head data is insufficient,there will be errors in inversion results with changing correlation scale.Due to the existence of correlation scale error,the improvement of inversion precision gradually slows down with the increase of the amount of observed head data,which indicates that too much observed head data causes data redundancy.Therefore,for the synthetic experiments described in this paper,the observation well number should be less than 41,the pumping test number should be less than 17,and a more suitable method should be selected according to the precision requirements of specific situations in practical engineering.展开更多
基金supported by the Guangxi Natural Science Foundation Program(2022GXNSFAA035583,2021GXNSFBA196052)the National Natural Science Foundation of China(32060305,32460270).
文摘Most research on carbon storage in forests has focused on qualitative studies of carbon storage and influ-encing factors rather than on quantifying the effect of the spatial distribution of carbon storage and of its influencing factors at different scales.Here we described the spatial dis-tribution of aboveground carbon storage(ACS)in a 20-ha plot in a subtropical evergreen broad-leaved forest to evalu-ate and quantify the relative effects of biotic factors(species diversity and structural diversity)and abiotic factors(soil and topographic factors)on ACS at different scales.Scale effects of the spatial distribution of ACS were significant,with higher variability at smaller scales,but less at larger scales.The distribution was also spatially heterogeneous,with more carbon storage on north-and east-facing slopes than on south-and west-facing slopes.At a smaller scale,species diversity and structural diversity each had a direct positive impact on ACS,but soil factors had no significant direct impact.At increasing scales,topographic and soil fac-tors gradually had a greater direct influence,whereas the influence of species diversity gradually decreased.Structural diversity had the greatest impact,followed by topographic factors and soil factors,while species diversity had a rela-tively smaller impact.These findings suggest studies on ACS in subtropical evergreen broadleaf forests in southern China should consider scale effects,specifically on the heterogene-ity of ACS distribution at small scales.Studies and conser-vation efforts need to focus on smaller habitat types with particular emphasis on habitat factors such as aspect and soil conditions,which have significant influences on community species diversity,structural diversity,and ACS distribution.
基金funded by the Guangdong Major Project of Basic and Applied Basic Research(2021B0301030007)the Supplemental Funds for Major Scientific Research Projects of Beijing Normal University,Zhuhai(ZHPT2023013)+1 种基金the National Natural Science Foundation of China(42301387)the Science and Technology Program of Guangdong(No.2024B1212070012)。
文摘The topographic factor(LS factor),derived from the multiplication of the slope length(L)and slope steepness(S)factors,is a vital parameter in soil erosion models.Generated from the digital elevation model(DEM),the LS factor always varies with the changing DEM resolution,i.e.,the LS factor scale effect.Previous studies have found the phenomenon of the LS factor scale effect,but the underlying causes of this phenomenon has not been well explored.Therefore,how the DEM resolution affects the LS factor and how the scale effect of the L and S factors influence the LS factor scale effect remains unclear.To address these problems,we collected 20 watersheds from the Guangdong Province with different topographic reliefs,and compared the corresponding L,S and LS factors at 10-m and 30-m resolution DEMs.Our results indicate that the S factor,heavily influenced by slope underestimation in coarse-resolution DEMs,makes a difference in the LS factor scale effect.In addition,the LS factor scale effect becomes less significant with increasing reliefs,suggesting the possibility of using 30-m DEM for LS calculation in rugged terrains.Our findings on the underlying mechanisms of the LS factor scale effect help to identify the uncertainty in the LS factor estimation,thereby enhancing the accuracy of soil erosion assessment,particularly in regions with different topographic characteristics and contribute to more effective soil conservation strategies and decision-making.
基金National Natural Science Foundation of China,No.42204031。
文摘With the continuous evolution of urban surface types,the impact of the urban heat island effect on the human population has intensified.Investigating the factors influencing urban thermal environments is crucial for providing theoretical support to urban planning and decision-making.In this study,Shenyang was selected to comprehensively analyse multiple factors,including topography,human activity,vegetation and landscape.Moreover,we used the random forest algorithm to explore nonlinear factors influencing land surface temperature(LST)over four years in the study area.The results revealed that from 2005 to 2020,the total areas with sub-high and high-temperature zones in northern Shenyang steadily increased.The area ratio of these zones increased from 20.18% in 2005 to 24.86% in 2020.Additionally,significant and strong correlations were observed between LST and variables such as the enhanced vegetation index(EVI),normalised difference vegetation index(NDVI),population density,proportion of cropland and proportion of impervious land.In 2010,proportion of impervious land exhibited the strongest correlation with LST at the 5 km scale,reaching 0.852(p<0.01).The 4 km grid scale was identified as the optimal grid size for this study,while the 2 km grid performed the worst.In 2020,NDVI emerged as the most significant factor influencing LST.These findings provide valuable guidance for improving urban planning and developing sustainable strategies.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB05040300)the National Natural Science Foundation of China(Grant No.41205119)
文摘Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.
基金Financial support received from the National Natural Science Foundation of China(22178379)the National Key Research and Development Program of China(2021YFC2800902)is gratefully acknowledged.
文摘Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.
基金Supported by the Joint Funds of the National Natural Science Foundation of China,No.U23A20434National Natural Science Foundation of China,No.82301738,No.82371535,and No.82171518+1 种基金the National Key Research and Development Program of China,No.2021YFF1201204the Science and Technology Innovation Program of Hunan Province,No.2023RC3083.
文摘BACKGROUND Antenatal depression is a disabling mental disorder among pregnant women and may cause adverse outcomes for both the mother and the offspring.Early identification and intervention of antenatal depression can help to prevent adverse outcomes.However,there have been few population-based studies focusing on the association of social and obstetric risk factors with antenatal depression in China.AIM To assess the sociodemographic and obstetric factors of antenatal depression and compare the network structure of depressive symptoms across different risk levels based on a large Chinese population.METHODS The cross-sectional survey was conducted in Shenzhen,China from 2020 to 2024.Antenatal depression was assessed using the Chinese version of the Edinburgh Postnatal Depression Scale(EPDS),with a score of≥13 indicating the presence of probable antenatal depression.Theχ2 test and binary logistic regression were used to identify the factors associated with antenatal depression.Network analyses were conducted to investigate the structure of depressive symptoms across groups with different risk levels.RESULTS Among the 44220 pregnant women,the prevalence of probable antenatal depression was 4.4%.An age≤24 years,a lower level of education(≤12 years),low or moderate economic status,having a history of mental disorders,being in the first trimester,being a primipara,unplanned pregnancy,and pregnancy without pre-pregnancy screening were found to be associated with antenatal depression(all P<0.05).Depressive symptom networks across groups with different risk levels revealed robust interconnections between symptoms.EPDS8("sad or miserable")and EPDS4("anxious or worried")showed the highest nodal strength across groups with different risk levels.CONCLUSION This study suggested that the prevalence of antenatal depression was 4.4%.Several social and obstetric factors were identified as risk factors for antenatal depression.EPDS8("sad or miserable")and EPDS4("anxious or worried")are pivotal targets for clinical intervention to alleviate the burden of antenatal depression.Early identification of highrisk groups is crucial for the development and implementation of intervention strategies to improve the overall quality of life for pregnant women.
基金The National High Technology Research and Development Program of China (863 Program)(No.2007AA01Z280)
文摘To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorithm is proposed. Based on the relationship between the iteration formulae, a new iteration formula is introduced, which leads the correction operation to be several simple shifting and adding operations. As one key part, the effects caused by rounding error are analyzed mathematically and it is concluded that the effects can be degraded by an appropriate selection of coefficients in the iteration formula. The model is then set up in Matlab and coded in Verilog HDL language. The proposed algorithm is also synthesized and verified in field-programmable gate array （FPGA）. The results show that this new scheme requires only one additional clock cycle and there is no change in the elementary iteration for the same precision compared with the conventional algorithm. In addition, the circuit realization is regular and the change in system throughput is very minimal.
基金National Natural Science Foundation of China(41130964)National Special Funding Project for Meteorology(GYHY-201006004)
文摘The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61007040)
文摘In order to analyze the effect of wavelength-dependent radiation-induced attenuation （RIA） on the mean trans- mission wavelength in optical fiber and the scale factor of interferometric fiber optic gyroscopes （IFOGs）, three types of polarization-maintaining （PM） fibers are tested by using a 60Co γ-radiation source. The observed different mean wave- length shift （MWS） behaviors for different fibers are interpreted by color-center theory involving dose rate-dependent absorption bands in ultraviolet and visible ranges and total dose-dependent near-infrared absorption bands. To evaluate the mean wavelength variation in a fiber coil and the induced scale factor change for space-borne IFOGs under low radiation doses in a space environment, the influence of dose rate on the mean wavelength is investigated by testing four germanium （Ge） doped fibers and two germanium-phosphorus （Ge-P） codoped fibers irradiated at different dose rates. Experimental results indicate that the Ge-doped fibers show the least mean wavelength shift during irradiation and their mean wavelength of optical signal transmission in fibers will shift to a shorter wavelength in a low-dose-rate radiation environment. Finally, the change in the scale factor of IFOG resulting from the mean wavelength shift is estimated and tested, and it is found that the significant radiation-induced scale factor variation must be considered during the design of space-borne IFOGs.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC1502103)the National Natural Science Foundation of China(Grant Nos.41430427 and 41705035)+1 种基金the China Scholarship Councilthe Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX17_0876)。
文摘This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB)of East China.The scale-dependent error growth(ensemble variability)and associated impact on precipitation forecasts(precipitation uncertainties)were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing.The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing.This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales.The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale,suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties,especially for the strong-forcing regime.Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors.Specifically,small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing.Meanwhile,larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale.Consequently,these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.
基金Supported by National Key R&D Program of China(Grant No.2018YFB1700704).
文摘Multifdelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from diferent fdelity data with a signifcant improvement in modeling efciency.In this paper,a modifed MFS(MMFS)model based on a radial basis function(RBF)is proposed,in which two fdelities of information can be analyzed by adaptively obtaining the scale factor.In the MMFS,an RBF was employed to establish the low-fdelity model.The correlation matrix of the high-fdelity samples and corresponding low-fdelity responses were integrated into an expansion matrix to determine the scaling function parameters.The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fdelity sample points.The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF)and single-fdelity RBF using four benchmark test functions,by which the impacts of diferent high-fdelity sample sizes on the prediction accuracy were also analyzed.The sensitivity of the MMFS model to the randomness of the design of experiments(DoE)was investigated by repeating sampling plans with 20 diferent DoEs.Stress analysis of the steel plate is presented to highlight the prediction ability of the proposed MMFS model.This research proposes a new multifdelity modeling method that can fully use two fdelity sample sets,rapidly calculate model parameters,and exhibit good prediction accuracy and robustness.
文摘This paper explains that the terms“horizontal and vertical scales”are not appropriate in map projections theory.Instead,the authors suggest using the term“scales in the direction of coordinate axes.”Since it is not possible to read a local linear scale factor in the direction of a coordinate axis immediately from the definition of a local linear scale factor,this paper considers the derivation of new formulae that enable local linear scale factors in the direction of coordinate x and y axes to be calculated.The formula for computing the local linear scale factor in any direction defined by dx and dy is also derived.Furthermore,the position and magnitude of the extreme values of the local linear scale factor are considered and new formulas derived.
基金financially supported by the National Natural Science Foundation of China(Grant No.51809273)。
文摘Four ships,a twin-propeller naval ship,two single-propeller container ships,and a single-propeller very large crude carrier(VLCC),were studied to investigate the scale effect of the form factor.The viscous flow fields of the ships at different scales were solved numerically via the Reynolds-averaged Navier–Stokes method combined with the shear stress transport k–ωturbulence model.The numerical method was validated through comparisons with experimental data,and numerical uncertainty analysis was carried out based on the ITTC recommended procedure.On this basis,scale effects of the form factor were analyzed using different friction lines,and scale effects of flow fields and the mean axial wake fractions were further analyzed in details.The results showed that the form factor exhibited scale effects when adopting the ITTC-1957 line,and it increased with the increase in the Reynolds number.The scale effect of the form factor reduces the prediction precision of the full-scale ship resistance.The friction line has a significant effect on the form factor.The form factor exhibits little dependence on the Reynolds number when using the numerical friction line or the Katsui line,which is useful for full-scale ship resistance predictions.With the increasing Reynolds number,the boundary layer thickness becomes thinner and the axial velocity contour contracts toward the center plane,and there is nearly a linear relationship between the reciprocal of mean axial wake fraction on propeller disc and Reynolds number in logarithmic scale for the three types of ship forms.
文摘This paper, divided into three parts (Part II-A, Part II-B and Part II-C), contains the detailed factorizational theory of asymptotic expansions of type (?)?, , , where the asymptotic scale?, , is assumed to be an extended complete Chebyshev system on a one-sided neighborhood of . It follows two pre-viously published papers: the first, labelled as Part I, contains the complete (elementary but non-trivial) theory for;the second is a survey highlighting only the main results without proofs. All the material appearing in §2 of the survey is here reproduced in an expanded form, as it contains all the preliminary formulas necessary to understand and prove the results. The remaining part of the survey—especially the heuristical considerations and consequent conjectures in §3—may serve as a good introduction to the complete theory.
基金National Natural Science Foundation of China through Grants(41461164008,41130964)National Key Project for Basic Research(973 Project)(2015452803)+1 种基金Science and Technology Planning Project for Guangdong Province(2012A061400012)China Meteorological Administration(GYHY201406009)
文摘In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The varia- tions of analysis increments with different-scale errors are consistent with those of variances and correlations of back- ground errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Tem- perature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more ac- curate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- （smaller-） scale errors will be beneficial for the accuracy of forecasts of heavy （light） precipitation at large （small） scales because of the ampli- fication （diminution） of the intensity and area in precipitation forecasts.
文摘Based on the data of 30 Chinese provinces for the period from 2004 to 2015,this paper expounds the carbon emissions effect of two-way foreign direct investment (FDI) from the perspective of scale effect and factor market distortions.This study uses Kaya identity to decompose carbon emission and construct simultaneous equations model to empirically examine the factor market distortion and the carbon emission scale effect of two-way FDI.The results show that the inward foreign direct investment (IFDI) increase regional carbon emission through scale effect and also exacerbates factor market distortion in China,whereas the outward FDI trends reduce carbon emission and reduces factor market distortions in China.The study also shows that human capital,research and development (R&D),trade openness,and capital accumulation are important determinants of two-way FDI.Therefore,the study proposes that IFDI policies should focus on acquiring green technologies.In addition,the domestic enterprises should be encouraged to participate in global business.
文摘The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.
基金the National Natural Science Foundation of China[Grant No.42071371]the National Key R&D Program of China[Grant Nos.2018YFB0505000 and 2018YFB0505400].
文摘The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results.It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values.To explore the impacts of the generalization scales,we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization(CAPSO)and applied them to simulate urban growth of Suzhou during 2000-2020.Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance(ERD)of the Generalized Additive Models(GAMs).Specifically,the ERD declined from 51.9%to 45.9%as the factor scale became larger during 2000-2020,but there was a peak value(52.2%)at Scale-2.For all simulations during 2000-2020,the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit(FOM),which respectively decreased by 3.1%and 4.4%as compared to the CA models with scale-free factors.We concluded that the driving factors at a smaller scale(200~400 m for point-like facilities and 7~14 m for line-like facilities)can build more accurate CA models to simulate urban growth patterns,and the optimal scale for factors can be identified using the ERD.This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.
基金This work was supported by the National Natural Science Foundation of China(Grants No.51879134 and 51569023)the First-class Discipline Construction Funding Project for the Ningxia University of China(Hydraulic Engineering)(Grant No.NXYLXK2017A03).
文摘In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivity field of a heterogeneous aquifer based on synthetic experiments.By increasing the numbers of observation wells and pumping tests,we analyzed the difference between the estimated and true values of hydraulic conductivity with different correlation scale errors.The relationships between the observation well number and the error in inversion results,and between the pumping test number and the error in inversion results were investigated.The results show that,if the amount of observed head data is insufficient,there will be errors in inversion results with changing correlation scale.Due to the existence of correlation scale error,the improvement of inversion precision gradually slows down with the increase of the amount of observed head data,which indicates that too much observed head data causes data redundancy.Therefore,for the synthetic experiments described in this paper,the observation well number should be less than 41,the pumping test number should be less than 17,and a more suitable method should be selected according to the precision requirements of specific situations in practical engineering.
