Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.A...Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.After a review of the existing theories of wall turbulence,we present a new framework,called the structure ensemble dynamics （SED）,which aims at integrating the turbulence dynamics into a quantitative description of the mean flow.The SED theory naturally evolves from a statistical physics understanding of non-equilibrium open systems,such as fluid turbulence, for which mean quantities are intimately coupled with the fluctuation dynamics.Starting from the ensemble-averaged Navier-Stokes（EANS） equations,the theory postulates the existence of a finite number of statistical states yielding a multi-layer picture for wall turbulence.Then,it uses order functions（ratios of terms in the mean momentum as well as energy equations） to characterize the states and transitions between states.Application of the SED analysis to an incompressible channel flow and a compressible turbulent boundary layer shows that the order functions successfully reveal the multi-layer structure for wall-bounded turbulence, which arises as a quantitative extension of the traditional view in terms of sub-layer,buffer layer,log layer and wake. Furthermore,an idea of using a set of hyperbolic functions for modeling transitions between layers is proposed for a quantitative model of order functions across the entire flow domain.We conclude that the SED provides a theoretical framework for expressing the yet-unknown effects of fluctuation structures on the mean quantities,and offers new methods to analyze experimental and simulation data.Combined with asymptotic analysis,it also offers a way to evaluate convergence of simulations.The SED approach successfully describes the dynamics at both momentum and energy levels, in contrast with all prevalent approaches describing the mean velocity profile only.Moreover,the SED theoretical framework is general,independent of the flow system to study, while the actual functional form of the order functions may vary from flow to flow.We assert that as the knowledge of order functions is accumulated and as more flows are analyzed, new principles（such as hierarchy,symmetry,group invariance,etc.） governing the role of turbulent structures in the mean flow properties will be clarified and a viable theory of turbulence might emerge.展开更多
Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation ind...Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index （NDVI） from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive （land restoration and re-vegetation） or negative （degradation） trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that： （1） land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; （2） the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; （3） compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.展开更多
In the last couple of years,there Has been an increased interest among the statisticians to dene new families of distributions by adding one or more additional parameter(s)to the baseline distribution.In this regard,a...In the last couple of years,there Has been an increased interest among the statisticians to dene new families of distributions by adding one or more additional parameter(s)to the baseline distribution.In this regard,a number of families have been introduced and studied.One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions.Whenever,we see a new method,the natural questions come in to mind are(i)what are the genesis of the newly proposed method and(ii)how did the proposed method is obtained.No doubt,the Marshall-Olkin family is a very useful method and has attracted the researchers.But,unfortunately,the authors failed to provide the explanation about the genesis of the method that how this family of distributions is obtained.To address this issue,in this article,an attempt Has been made to provide a straight forward computation about the genesis of the Marshall-Olkin family that somehow completes its derivation.The genesis of the Marshall-Olkin family is based on the T-X family approach.Furthermore,we have showed that other extensions of the Marshall-Olkin family can also be obtained via the T-X family method.Finally,a real-life application form insurance science is presented to illustrate the newly proposed extension of the Marshall-Olkin family.展开更多
We consider intrinsic gate capacitance variations due to random dopants in the nanometer metal oxide semi- conductor field effect transistor （MOSFET） channel. The variations of total gate capacitance and gate transc...We consider intrinsic gate capacitance variations due to random dopants in the nanometer metal oxide semi- conductor field effect transistor （MOSFET） channel. The variations of total gate capacitance and gate transcapacitances are investigated and the strong correlations between the trans-capacitance variations are discovered. A simple statistical model is proposed for accurately capturing total gate capacitance variability based on the correlations. The model fits very well with the Monte Carlo simulations and the average errors are -0.033% for n-type metal-oxide semiconductor and -0.012% for p-type metal-oxide semiconductor, respectively. Our simulation studies also indicate that, owing to these correlations, the total gate capacitance variability will not dominate in gate capacitance variations.展开更多
Barchan dunes are among the most common accumulative phenomena made by wind erosion,which are usually formed in regions where the prevailing wind direction is almost constant throughout the year and there is not enoug...Barchan dunes are among the most common accumulative phenomena made by wind erosion,which are usually formed in regions where the prevailing wind direction is almost constant throughout the year and there is not enough sand to completely cover the land surface.Barchans are among the most common windy landscapes in Pashoueyeh Erg in the west of Lut Desert,Iran.This study aims to elaborate on morphological properties of barchans in this region using mathematical and statistical models.The results of these methods are very important in investigating barchan shapes and identifying their behavior.Barchan shapes were mathematically modeled by simulating them in the coordinate system through nonlinear parabolic equations,so that two separate equations were calculated for barchan windward and slip-face parabolas.The type and intensity of relationships between barchan morphology and mathematical parameters were determined by the statistical modeling.The results indicated that the existing relationships followed the power correlation with the maximum coefficient of determination and minimum error of estimate.Combining the above two methods is a powerful basis for stimulating barchans in virtual and laboratory environments.The most important result of this study is to convert the mathematical and statistical models of barchan morphology to each other.Focal length is one of the most important parameters of barchan parabolas,suggesting different states of barchans in comparison with each other.As the barchan's focal length decreases,its opening becomes narrower,and the divergence of the barchan's horns reduces.Barchans with longer focal length have greater width,dimensions,and volume.In general,identifying and estimating the morphometric and planar parameters of barchans is effective in how they move,how much they move,and how they behave in the environment.These cases play an important role in the management of desert areas.展开更多
<strong>Background:</strong> The Cox Proportional Hazard (Cox-PH) model has been a popularly used method for survival analysis of cancer data given the survival times as a function of covariates or risk fa...<strong>Background:</strong> The Cox Proportional Hazard (Cox-PH) model has been a popularly used method for survival analysis of cancer data given the survival times as a function of covariates or risk factors. However, it is very seldom to see the assumptions for the application of the Cox-PH model satisfied in most of the research studies, raising questions about the effectiveness, robustness, and accuracy of the model predicting the proportion of survival times. This is because the necessary assumptions in most cases are difficult to satisfy, as well as the assessment of interaction among covariates. <strong>Methods:</strong> To further improve the therapeutic/treatment strategy for cancer diseases, we proposed a new approach to survival analysis using multiple myeloma (MM) cancer data. We first developed a data-driven nonlinear statistical model that predicts the survival times with 93% accuracy. We then performed a parametric analysis on the predicted survival times to obtain the survival function which is used in estimating the proportion of survival times. <strong>Results:</strong> The new proposed approach for survival analysis has proved to be more robust and gives better estimates of the proportion of survival than the Cox-PH model. Also, satisfying the proposed model assumptions and finding interactions among risk factors is less difficult compared to the Cox-PH model. The proposed model can predict the real values of the survival times and the identified risk factors are ranked according to the percent of contribution to the survival time. <strong>Conclusion:</strong> The new proposed nonlinear statistical model approach for survival analysis of cancer diseases is very efficient and provides an improved and innovative strategy for cancer therapeutic/treatment.展开更多
This study explores a comprehensive statistical model for analyzing rental apartment prices per square meter in Munich,Germany.The research investigates key quantitative and qualitative variables influencing rent dyna...This study explores a comprehensive statistical model for analyzing rental apartment prices per square meter in Munich,Germany.The research investigates key quantitative and qualitative variables influencing rent dynamics by leveraging a robust dataset comprising over 2.6 million apartments with 59 variables,sourced from FDZ Ruhr and ImmobilienScout24,for the years 2015 and 2019.Thirty-one key variables(9 quantitative and 22 qualitative)were analyzed,and the study identified significant predictors,such as apartment size,furnishing quality,energy efficiency,and amenity availability,through exploratory data analysis and multiple linear regression with nonlinear covariates.Applying log transformations and polynomial terms improved model performance,with the 2019 model achieving an adjusted R-squared of over 0.54 in the Analysis Of Variance(ANOVA)ratio tests.Model diagnostics,including the Akaike Information Criterion(AIC),residual plots,and Variance Inflation Factor(VIF),were employed to assess model fit and multicollinearity,ensuring the robustness and validity of the regression model.The results indicate a consistent trend where larger apartments and permitting pets command lower rent per square meter,while upscale furnishings,kitchens,and the number of bedrooms are associated with higher prices.This study provides meaningful predictive analytics insights into urban housing and Munich’s evolving rental market.The findings provide valuable insights for real estate planning,sustainable housing policies,urban development strategies,and educators,particularly for university administrators and planners who can advocate for informed housing policies.This research contributes to academic literature on rent modeling and provides a data-driven foundation for evidence-based decision-making in high-demand urban housing markets.展开更多
In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions...In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a ...Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.展开更多
Given that the strength of statistics lies in modelling,we are motivated to do a comparative statistical study between two types of ragas,one being aesthetically known to be restful and the other restless in nature.We...Given that the strength of statistics lies in modelling,we are motivated to do a comparative statistical study between two types of ragas,one being aesthetically known to be restful and the other restless in nature.We first try to distinguish these two types through statistical modeling.To substantiate our findings,two more sta-tistical features are considered in the paper to separate the two categories of ragas,namely the rate of change of pitch and inter-onset interval.The experimental results are encouraging.展开更多
The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were qua...The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were quantified under strongly acidic conditions(pH 2)at 298-328 K.The adsorption equilibria of these earth elements were analyzed using two statistical physics models(homogeneous and heterogeneous monolayer models).The results show that the adsorption of these ions implies a multiionic mechanism,which is exothermic.Si-containing functional groups are responsible for the adsorption of these rare-earth elements on the diatomite surface.A heterogeneous statistical physics model confirms that two Si-based functional groups participate in the separation of these cations.The calculated adsorption capacities at saturation follow the order:neodymium>cerium>lanthanum.Calculated interaction energies range from 28600 to 40100 J/mol,indicating physical adsorption on diatomite M45.This study demonstrates that diatomite M45 is a promising separation medium that can be used for the recovery of REEs dissolved in aqueous solutions via adsorption.展开更多
The treatment and disposal of radioactive waste are presently facing great challenges.Spent ion exchange resins have become a focus of attention due to their high production and serious environmental risks.In this pap...The treatment and disposal of radioactive waste are presently facing great challenges.Spent ion exchange resins have become a focus of attention due to their high production and serious environmental risks.In this paper,a simplified model of cationic exchange resin is proposed,and the degradation processes of cationic resin monomer initiated by hydroxyl radicals(·OH)are clarified by combining statistical molecular fragmentation(SMF)model and density functional theory(DFT)calculations.The prediction of active sites indicates that the S-O bonds and the C-S bond of the sulfonic group are more likely to react during the degradation.The meta-position of the sulfonic group on the benzene ring is the most active site,and the benzene ring without the sulfonic group has a certain reactivity.The C11-C14 and C17-C20 bonds,on the carbon skeleton,are the most easily broken.It is also found that dihydroxy addition and elimination reactions play a major role in the process of desulfonation,carbon skeleton cleavage and benzene ring separation.The decomposition mechanisms found through the combination of physical models and chemical calculations,provide theoretical guidance for the treatment of complex polycyclic aromatic hydrocarbons.展开更多
The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as ...The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as an initial estimate of ovarian age. A total of 28,016 women on the territory of the Republic of Bulgaria were tested for serum AMH levels with a median age of 37.0 years (interquartile range 32.0 to 41.0). For women aged 20 - 29 years, the Bulgarian population has relatively high median levels of AMH, similar to women of Asian origin. For women aged 30 - 34 years, our results are comparable to those of women living in Western Europe. For women aged 35 - 39 years, our results are comparable to those of women living in the territory of India and Kenya. For women aged 40 - 44 years, our results were lower than those for women from the Western European and Chinese populations, close to the Indian and higher than Korean and Kenya populations, respectively. Our results for women of Bulgarian origin are also comparable to US Latina women at age 30, 35 and 40 ages. On the base on constructed a statistical model to predicting the decline in AMH levels at different ages, we found non-linear structure of AMH decline for the low AMH 3.5) the dependence of the decline of AMH on age was confirmed as linear. In conclusion, we evaluated the serum level of AMH in Bulgarian women and established age-specific AMH percentile reference values based on a large representative sample. We have developed a prognostic statistical model that can facilitate the application of AMH in clinical practice and the prediction of reproductive capacity and population health.展开更多
In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and...In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.展开更多
Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale....Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.展开更多
Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of th...Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.展开更多
基金supported by the National Natural Science Foundation of China(90716008)the National Basic Research Program of China(2009CB724100).
文摘Despite dedicated effort for many decades,statistical description of highly technologically important wall turbulence remains a great challenge.Current models are unfortunately incomplete,or empirical,or qualitative.After a review of the existing theories of wall turbulence,we present a new framework,called the structure ensemble dynamics （SED）,which aims at integrating the turbulence dynamics into a quantitative description of the mean flow.The SED theory naturally evolves from a statistical physics understanding of non-equilibrium open systems,such as fluid turbulence, for which mean quantities are intimately coupled with the fluctuation dynamics.Starting from the ensemble-averaged Navier-Stokes（EANS） equations,the theory postulates the existence of a finite number of statistical states yielding a multi-layer picture for wall turbulence.Then,it uses order functions（ratios of terms in the mean momentum as well as energy equations） to characterize the states and transitions between states.Application of the SED analysis to an incompressible channel flow and a compressible turbulent boundary layer shows that the order functions successfully reveal the multi-layer structure for wall-bounded turbulence, which arises as a quantitative extension of the traditional view in terms of sub-layer,buffer layer,log layer and wake. Furthermore,an idea of using a set of hyperbolic functions for modeling transitions between layers is proposed for a quantitative model of order functions across the entire flow domain.We conclude that the SED provides a theoretical framework for expressing the yet-unknown effects of fluctuation structures on the mean quantities,and offers new methods to analyze experimental and simulation data.Combined with asymptotic analysis,it also offers a way to evaluate convergence of simulations.The SED approach successfully describes the dynamics at both momentum and energy levels, in contrast with all prevalent approaches describing the mean velocity profile only.Moreover,the SED theoretical framework is general,independent of the flow system to study, while the actual functional form of the order functions may vary from flow to flow.We assert that as the knowledge of order functions is accumulated and as more flows are analyzed, new principles（such as hierarchy,symmetry,group invariance,etc.） governing the role of turbulent structures in the mean flow properties will be clarified and a viable theory of turbulence might emerge.
基金National Basic Research Program of China (2012CB722201)National Natural Science Foundation of China (30970504, 31060320)National Science and Technology Support Program (2011BAC07B01)
文摘Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index （NDVI） from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive （land restoration and re-vegetation） or negative （degradation） trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that： （1） land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; （2） the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; （3） compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.
基金supported by the Department of Statistics,Yazd University,Yazd,Iran。
文摘In the last couple of years,there Has been an increased interest among the statisticians to dene new families of distributions by adding one or more additional parameter(s)to the baseline distribution.In this regard,a number of families have been introduced and studied.One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions.Whenever,we see a new method,the natural questions come in to mind are(i)what are the genesis of the newly proposed method and(ii)how did the proposed method is obtained.No doubt,the Marshall-Olkin family is a very useful method and has attracted the researchers.But,unfortunately,the authors failed to provide the explanation about the genesis of the method that how this family of distributions is obtained.To address this issue,in this article,an attempt Has been made to provide a straight forward computation about the genesis of the Marshall-Olkin family that somehow completes its derivation.The genesis of the Marshall-Olkin family is based on the T-X family approach.Furthermore,we have showed that other extensions of the Marshall-Olkin family can also be obtained via the T-X family method.Finally,a real-life application form insurance science is presented to illustrate the newly proposed extension of the Marshall-Olkin family.
基金Supported by the National Natural Science Foundation of China under Grant Nos 61271064,61571171 and 61302009the Zhejiang Provincial Natural Science Foundation of China under Grant No LZ12F01001
文摘We consider intrinsic gate capacitance variations due to random dopants in the nanometer metal oxide semi- conductor field effect transistor （MOSFET） channel. The variations of total gate capacitance and gate transcapacitances are investigated and the strong correlations between the trans-capacitance variations are discovered. A simple statistical model is proposed for accurately capturing total gate capacitance variability based on the correlations. The model fits very well with the Monte Carlo simulations and the average errors are -0.033% for n-type metal-oxide semiconductor and -0.012% for p-type metal-oxide semiconductor, respectively. Our simulation studies also indicate that, owing to these correlations, the total gate capacitance variability will not dominate in gate capacitance variations.
文摘Barchan dunes are among the most common accumulative phenomena made by wind erosion,which are usually formed in regions where the prevailing wind direction is almost constant throughout the year and there is not enough sand to completely cover the land surface.Barchans are among the most common windy landscapes in Pashoueyeh Erg in the west of Lut Desert,Iran.This study aims to elaborate on morphological properties of barchans in this region using mathematical and statistical models.The results of these methods are very important in investigating barchan shapes and identifying their behavior.Barchan shapes were mathematically modeled by simulating them in the coordinate system through nonlinear parabolic equations,so that two separate equations were calculated for barchan windward and slip-face parabolas.The type and intensity of relationships between barchan morphology and mathematical parameters were determined by the statistical modeling.The results indicated that the existing relationships followed the power correlation with the maximum coefficient of determination and minimum error of estimate.Combining the above two methods is a powerful basis for stimulating barchans in virtual and laboratory environments.The most important result of this study is to convert the mathematical and statistical models of barchan morphology to each other.Focal length is one of the most important parameters of barchan parabolas,suggesting different states of barchans in comparison with each other.As the barchan's focal length decreases,its opening becomes narrower,and the divergence of the barchan's horns reduces.Barchans with longer focal length have greater width,dimensions,and volume.In general,identifying and estimating the morphometric and planar parameters of barchans is effective in how they move,how much they move,and how they behave in the environment.These cases play an important role in the management of desert areas.
文摘<strong>Background:</strong> The Cox Proportional Hazard (Cox-PH) model has been a popularly used method for survival analysis of cancer data given the survival times as a function of covariates or risk factors. However, it is very seldom to see the assumptions for the application of the Cox-PH model satisfied in most of the research studies, raising questions about the effectiveness, robustness, and accuracy of the model predicting the proportion of survival times. This is because the necessary assumptions in most cases are difficult to satisfy, as well as the assessment of interaction among covariates. <strong>Methods:</strong> To further improve the therapeutic/treatment strategy for cancer diseases, we proposed a new approach to survival analysis using multiple myeloma (MM) cancer data. We first developed a data-driven nonlinear statistical model that predicts the survival times with 93% accuracy. We then performed a parametric analysis on the predicted survival times to obtain the survival function which is used in estimating the proportion of survival times. <strong>Results:</strong> The new proposed approach for survival analysis has proved to be more robust and gives better estimates of the proportion of survival than the Cox-PH model. Also, satisfying the proposed model assumptions and finding interactions among risk factors is less difficult compared to the Cox-PH model. The proposed model can predict the real values of the survival times and the identified risk factors are ranked according to the percent of contribution to the survival time. <strong>Conclusion:</strong> The new proposed nonlinear statistical model approach for survival analysis of cancer diseases is very efficient and provides an improved and innovative strategy for cancer therapeutic/treatment.
文摘This study explores a comprehensive statistical model for analyzing rental apartment prices per square meter in Munich,Germany.The research investigates key quantitative and qualitative variables influencing rent dynamics by leveraging a robust dataset comprising over 2.6 million apartments with 59 variables,sourced from FDZ Ruhr and ImmobilienScout24,for the years 2015 and 2019.Thirty-one key variables(9 quantitative and 22 qualitative)were analyzed,and the study identified significant predictors,such as apartment size,furnishing quality,energy efficiency,and amenity availability,through exploratory data analysis and multiple linear regression with nonlinear covariates.Applying log transformations and polynomial terms improved model performance,with the 2019 model achieving an adjusted R-squared of over 0.54 in the Analysis Of Variance(ANOVA)ratio tests.Model diagnostics,including the Akaike Information Criterion(AIC),residual plots,and Variance Inflation Factor(VIF),were employed to assess model fit and multicollinearity,ensuring the robustness and validity of the regression model.The results indicate a consistent trend where larger apartments and permitting pets command lower rent per square meter,while upscale furnishings,kitchens,and the number of bedrooms are associated with higher prices.This study provides meaningful predictive analytics insights into urban housing and Munich’s evolving rental market.The findings provide valuable insights for real estate planning,sustainable housing policies,urban development strategies,and educators,particularly for university administrators and planners who can advocate for informed housing policies.This research contributes to academic literature on rent modeling and provides a data-driven foundation for evidence-based decision-making in high-demand urban housing markets.
基金supported by TUBITAK 112E568,114E092,and 115E915 projectsTNPGN-Active RINEX data set is available to the IONOLAB group for the TUBITAK 109E055 project。
文摘In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金the National Key Research and Development Program of China(No.2021YFC3100800)the National Natural Science Foundation of China(Nos.42407235 and 42271026)the Project of Sanya Yazhou Bay Science and Technology City(No.SCKJ-JYRC-2023-54).
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金Project(52274096)supported by the National Natural Science Foundation of ChinaProject(WS2023A03)supported by the State Key Laboratory Cultivation Base for Gas Geology and Gas Control,China。
文摘Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.
文摘Given that the strength of statistics lies in modelling,we are motivated to do a comparative statistical study between two types of ragas,one being aesthetically known to be restful and the other restless in nature.We first try to distinguish these two types through statistical modeling.To substantiate our findings,two more sta-tistical features are considered in the paper to separate the two categories of ragas,namely the rate of change of pitch and inter-onset interval.The experimental results are encouraging.
基金The Research Center for Advanced Materials Science (RCAMS)at King Khalid University,Saudi Arabia is acknowledged for funding this work under the grant number RCAMS/KKU/016-22。
文摘The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were quantified under strongly acidic conditions(pH 2)at 298-328 K.The adsorption equilibria of these earth elements were analyzed using two statistical physics models(homogeneous and heterogeneous monolayer models).The results show that the adsorption of these ions implies a multiionic mechanism,which is exothermic.Si-containing functional groups are responsible for the adsorption of these rare-earth elements on the diatomite surface.A heterogeneous statistical physics model confirms that two Si-based functional groups participate in the separation of these cations.The calculated adsorption capacities at saturation follow the order:neodymium>cerium>lanthanum.Calculated interaction energies range from 28600 to 40100 J/mol,indicating physical adsorption on diatomite M45.This study demonstrates that diatomite M45 is a promising separation medium that can be used for the recovery of REEs dissolved in aqueous solutions via adsorption.
基金supported by the National Natural Science Foundation of China (No.22176067).
文摘The treatment and disposal of radioactive waste are presently facing great challenges.Spent ion exchange resins have become a focus of attention due to their high production and serious environmental risks.In this paper,a simplified model of cationic exchange resin is proposed,and the degradation processes of cationic resin monomer initiated by hydroxyl radicals(·OH)are clarified by combining statistical molecular fragmentation(SMF)model and density functional theory(DFT)calculations.The prediction of active sites indicates that the S-O bonds and the C-S bond of the sulfonic group are more likely to react during the degradation.The meta-position of the sulfonic group on the benzene ring is the most active site,and the benzene ring without the sulfonic group has a certain reactivity.The C11-C14 and C17-C20 bonds,on the carbon skeleton,are the most easily broken.It is also found that dihydroxy addition and elimination reactions play a major role in the process of desulfonation,carbon skeleton cleavage and benzene ring separation.The decomposition mechanisms found through the combination of physical models and chemical calculations,provide theoretical guidance for the treatment of complex polycyclic aromatic hydrocarbons.
文摘The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as an initial estimate of ovarian age. A total of 28,016 women on the territory of the Republic of Bulgaria were tested for serum AMH levels with a median age of 37.0 years (interquartile range 32.0 to 41.0). For women aged 20 - 29 years, the Bulgarian population has relatively high median levels of AMH, similar to women of Asian origin. For women aged 30 - 34 years, our results are comparable to those of women living in Western Europe. For women aged 35 - 39 years, our results are comparable to those of women living in the territory of India and Kenya. For women aged 40 - 44 years, our results were lower than those for women from the Western European and Chinese populations, close to the Indian and higher than Korean and Kenya populations, respectively. Our results for women of Bulgarian origin are also comparable to US Latina women at age 30, 35 and 40 ages. On the base on constructed a statistical model to predicting the decline in AMH levels at different ages, we found non-linear structure of AMH decline for the low AMH 3.5) the dependence of the decline of AMH on age was confirmed as linear. In conclusion, we evaluated the serum level of AMH in Bulgarian women and established age-specific AMH percentile reference values based on a large representative sample. We have developed a prognostic statistical model that can facilitate the application of AMH in clinical practice and the prediction of reproductive capacity and population health.
文摘In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.
文摘Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.
基金financial support from the University of Wyomingthe School of Energy Resources for this research is greatly acknowledged。
文摘Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.
