In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to...In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to <em>n</em>. We mainly discuss the conditions under which an <em>n</em>-dimensional overlap function can be expressed in terms of its generator pair.展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bam...The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bamboo.In this study,we investigated the height and root collar diameter(RCD)growth of Sakhalin fir seedlings under various degrees of cover by deciduous vegetation and evergreen dwarf bamboo.Generalized additive models were used to quantify the effects of canopy cover and forest floor cover on the relative growth rates of these two parameters.The canopy cover of Sakhalin fir seedlings had a nonlin-ear negative effect on both the height growth of seedlings in the subsequent year and the RCD growth in the current year,given the general growth pattern in this species,where height growth ceases in early summer and RCD growth con-tinues until autumn.Height growth declined sharply after the canopy cover rate exceeded 50%,while RCD growth declined rapidly between 0 and 50%canopy cover rate.The forest floor cover had a greater negative impact on RCD growth than on height growth.These results suggested that Sakhalin fir seedlings respond to vegetative competition by prioritizing height growth for light acquisition at the expense of diameter growth and possibly root growth for below-ground competition.The cover of evergreen dwarf bamboo reduced the height growth of fir seedlings significantly more than the cover of deciduous vegetation.This difference is likely due to the timing of light availability.When competing with deciduous vegetation,Sakhalin fir seedlings exposed to light during the post-snow melt and early spring before the development of the deciduous vegetation canopy can photosynthesize more effectively,leading to greater height growth.The results of this study highlighted the importance of vegetation control considering the type of vegetation for successful Sakhalin fir reforestation.Adjusting the intensity and timing of weeding based on the presence and abundance of dwarf bamboo and other competing vegetation could potentially reduce weeding costs and increase biodiversity in reforested areas.展开更多
Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air qual...Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow<1200 vehicles per hour.This study also classified rainfall events into four types depending on its characteristic.Wet deposition from varied rainfall types significantly affects air quality,with TypeⅠrainfall events(long-duration heavy rain)having the most pronounced impact.An LSTM model incorporating GAM and Bayesian network outcomes yields excellent performance,achieving correlation R^(2)>0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO_(2),and NO_(x);and O_(3),PM_(10),and PM_(2.5),respectively.展开更多
To investigate the response of Roadside Monitoring Stations(RSs)to traffic-related air pollution,traffic and pollutant characteristics,influencing factors,and potential source characterization in Tianjin,China were de...To investigate the response of Roadside Monitoring Stations(RSs)to traffic-related air pollution,traffic and pollutant characteristics,influencing factors,and potential source characterization in Tianjin,China were determined based on roadside monitoring of real-world data conducted at RSs in 2022.The diurnal variation trend of pollutants at RSs was consistent with that at the National Monitoring Station(NM),with notably higher pollutant fluctuations during the morning and evening peak traffic times at RSs,where the average diurnal concentration was 41.46%higher than that at the NM.The generalized additive model(GAM)for nitrogen oxides(NO_(x))and carbon monoxide(CO),responding to themultiple influencing factors,performed well at RSs,with deviance explained by 86.6%and 61.4%,respectively.The synergistic effects of wind direction and speed contributed to most of the variations in NO_(x) and CO,which were 14.74%and 12.87%,respectively.Pollutant concentrations were highest under windless conditions,with pollutants originating primarily from local vehicle emissions.The model results indicated that medium-duty truck(MDT)traffic flow predominantly contributed to the variability in NO_(x) emissions,whereas passenger car(PC)traffic flow was the primary source of CO emissions from traffic variables.MDTs should be the focus of urban NO_(x) traffic emissions control.Potential-source analysis validated the results obtained from the GAM,and both analyses showed that RSs can better characterize traffic-related air pollutants.Furthermore,more stringent emission standards have effectively mitigated the release of pollutants from motor vehicles and contributed to the modernization of vehicle fleet composition,effectively decreasing CO concentrations.展开更多
Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) ...Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.展开更多
Recent years have witnessed increasingly frequent extreme precipitation events,especially in desert steppes in the semi-arid and arid transition zone.Focusing on a desert steppe in western-central Inner Mongolia Auton...Recent years have witnessed increasingly frequent extreme precipitation events,especially in desert steppes in the semi-arid and arid transition zone.Focusing on a desert steppe in western-central Inner Mongolia Autonomous Region,China,this study aimed to determine the principle time-varying pattern of extreme precipitation and its dominant climate forcings during the period 1988-2017.Based on the generalized additive models for location,scale,and shape(GAMLSS)modeling framework,we developed the best time-dependent models for the extreme precipitation series at nine stations,as well as the optimized non-stationary models with large-scale climate indices(including the North Atlantic Oscillation(NAO),Atlantic Multidecadal Oscillation(AMO),Southern Oscillation(SO),Pacific Decadal Oscillation(PDO),Arctic Oscillation(AO),and North Pacific Oscillation(NPO))as covariates.The results indicated that extreme precipitation remained stationary at more than half of the stations(Hailisu,Wuyuan,Dengkou,Hanggin Rear Banner,Urad Front Banner,and Yikewusu),while linear and non-linear time-varying patterns were quantitatively identified at the other stations(Urad Middle Banner,Linhe,and Wuhai).These non-stationary behaviors of extreme precipitation were mainly reflected in the mean value of extreme precipitation.The optimized non-stationary models performed best,indicating the significant influences of large-scale climate indices on extreme precipitation.In particular,the NAO,NPO,SO,and AMO remained as covariates and significantly influenced the variations in the extreme precipitation regime.Our findings have important reference significance for gaining an in-depth understanding of the driving mechanism of the non-stationary behavior of extreme precipitation and enable advanced predictions of rainstorm risks.展开更多
This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vege...This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.展开更多
This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with gener...This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.展开更多
A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of ...A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.展开更多
Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on ri...Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.展开更多
There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution i...There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.展开更多
In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes...In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.展开更多
Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments.As an example,Iranian montane ranges are highly diverse,particularly in the Irano-Turanian phytogeographical re...Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments.As an example,Iranian montane ranges are highly diverse,particularly in the Irano-Turanian phytogeographical region.Understanding plant diversity patterns with increasing elevation is of high significance,not least for conservation planning.We studied the pattern of species richness,Shannon diversity,endemic richness,endemics ratio,and richness of life forms along a 3900 m elevational transect in Mount Palvar,overlooking the Lut Desert in Southeast Iran.We also analyzed the effect of environmental variables on species turnover along the vertical gradient.A total of 120 vegetation plots(10 m×10 m)were sampled along the elevational transect containing species and environmental data.To discover plant diversity pattern along the elevational gradient,generalized additive model(GAM)was used.Non-metric multidimensional scaling(NMDS)was applied for illustrating the correlation between species composition and environmental variables.We found hump-shaped pattern for species richness,Shannon diversity,endemic richness,and species richness of different life forms,but a monotonic increasing pattern for ratio of endemic species from low to high elevations.Our study confirms the humped pattern of species richness peaking at intermediate elevations along a complete elevational gradient in a semi-arid mountain.The monotonic increase of endemics ratio with elevation in our area as a case study is consistent with global increase of endemism with elevation.According to our results,temperature and precipitation are two important climatic variables that drive elevational plant diversity,particularly in seasonally dry areas.Our study suggests that effective conservation and management are needed for this low latitude mountain area along with calling for long-term monitoring for species redistribution.展开更多
Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands i...Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.展开更多
Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management...Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.展开更多
Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth t...Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.展开更多
In Central Europe,anthropogenic coniferous monocultures have been subject to conversion to more diverse mixed forests since the 1990s,however,they are still abundant across many forest landscapes.Artificial and natura...In Central Europe,anthropogenic coniferous monocultures have been subject to conversion to more diverse mixed forests since the 1990s,however,they are still abundant across many forest landscapes.Artificial and natural tree regeneration both play a key role during conversion by determining the species composition and structure of the future forests.Many abiotic and biotic factors can potentially influence the regeneration process and its specific combinations or interactions may be different among tree species and its developmental stages.Here,we aimed to identify and quantify the effect of the most important drivers on the density of the most abundant regenerating tree species(i.e.,Norway spruce and European beech),as well as on species and structural diversity of the tree regeneration.We studied tree regeneration in four former monospecific coniferous stand types(i.e.,Norway spruce,Scots pine,European larch,and Douglas fir)in Southwest Germany that have been under conversion to mixed forests since the 1990s.We sampled tree regeneration in four growth height classes together with a variety of potentially influencing factors on 108 sampling plots and applied multivariate analyses.We identified light availability in the understorey,stand structural attributes,browsing pressure,and diaspore source abundance as the most important factors for the density and diversity of tree regeneration.Particularly,we revealed speciesspecific differences in drivers of regeneration density.While spruce profited from increasing light availability and decreasing stand basal area,beech benefited either from a minor reduction or more strikingly from an increase in overstorey density.Increasing diaspore source abundance positively and a high browsing pressure negatively affected both species equally.Our results suggest that humus and topsoil properties were modified during conversion,probably due to changes in tree species composition and silvicultural activities.The species and structural diversity of the tree regeneration benefitted from increasing light availability,decreasing stand basal area,and a low to moderate browsing pressure.We conclude that forest managers may carefully equilibrate among the regulation of overstorey cover,stand basal area,and browsing pressure to fulfil the objectives of forest conversion,i.e.,establishing and safeguarding a diverse tree regeneration to promote the development of mature mixed forests in the future.展开更多
This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that...This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that em-ployed Frequentist and Bayesian Linear Mixed Model,continent-specific ran-dom slopes for exchange rate were incorporated to assess the variability in the rate at which changes in exchange rate influence inflation.Fixed effects cap-tured the overall impact of the predictors,while random effects accounted for regional differences.Results consistently showed that the monetary policy rate significantly affects inflation,whereas the exchange rate does not.Strong evi-dence supported variation in baseline inflation across continents(random in-tercepts),but findings on random slope variability were mixed,suggesting modest and model-dependent heterogeneity.Bayesian models offered a slightly better fit and predictive accuracy.These findings underscore the central role of monetary policy in inflation control,while exchange rate effects remain context-dependent.These results highlight the importance of accounting for regional heterogeneity when modelling global inflation dynamics.Policymak-ers should tailor inflation strategies to regional contexts and prioritize robust monetary policy tools over exchange rate management.These findings are as-sociational,not causal and future research should adopt a credible causal iden-tification strategy to establish causal relationships.展开更多
The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature （SST）, chlorophyll c...The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature （SST）, chlorophyll concentration （Chl-α） and sea surface height （SSH） from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10℃-22℃ and the favorite temperature is between 15℃-17℃. The Chl-α concentration is 0.1-0.6 mg/m^3. When Chl-α concentration changes to 0.12-0.14 mg/m^3, the probability of forming fishing ground becomes very high. In most fishing grounds, the SSH is higher than the mean SSH. The generalized additive model （GAM） was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October-November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude.展开更多
文摘In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to <em>n</em>. We mainly discuss the conditions under which an <em>n</em>-dimensional overlap function can be expressed in terms of its generator pair.
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
基金supported by the Ministry of Agriculture,Forestry,and Fisheries of Japan (25093 C)JSPS KAKENHI (JP23H02262)
文摘The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bamboo.In this study,we investigated the height and root collar diameter(RCD)growth of Sakhalin fir seedlings under various degrees of cover by deciduous vegetation and evergreen dwarf bamboo.Generalized additive models were used to quantify the effects of canopy cover and forest floor cover on the relative growth rates of these two parameters.The canopy cover of Sakhalin fir seedlings had a nonlin-ear negative effect on both the height growth of seedlings in the subsequent year and the RCD growth in the current year,given the general growth pattern in this species,where height growth ceases in early summer and RCD growth con-tinues until autumn.Height growth declined sharply after the canopy cover rate exceeded 50%,while RCD growth declined rapidly between 0 and 50%canopy cover rate.The forest floor cover had a greater negative impact on RCD growth than on height growth.These results suggested that Sakhalin fir seedlings respond to vegetative competition by prioritizing height growth for light acquisition at the expense of diameter growth and possibly root growth for below-ground competition.The cover of evergreen dwarf bamboo reduced the height growth of fir seedlings significantly more than the cover of deciduous vegetation.This difference is likely due to the timing of light availability.When competing with deciduous vegetation,Sakhalin fir seedlings exposed to light during the post-snow melt and early spring before the development of the deciduous vegetation canopy can photosynthesize more effectively,leading to greater height growth.The results of this study highlighted the importance of vegetation control considering the type of vegetation for successful Sakhalin fir reforestation.Adjusting the intensity and timing of weeding based on the presence and abundance of dwarf bamboo and other competing vegetation could potentially reduce weeding costs and increase biodiversity in reforested areas.
基金supported by the Ministry of Environment(Environmental Protection Administration),Taiwan(Projects EPA-106-L103-02-A022,EPA-106-L102-02-A142)the"National"Science and Technology Council(Ministry of Science and Technology),Taiwan(Nos.108-2625-M-008-002,108-2119-M-008-003,108-2636-E-008-004,109-2636-E-008-008,110-2636-E-008-006,111-2636-E-008-014,and 112-2636-E-008-005(Young Scholar Fellowship Program),112-2119-M-008-010,and 108-2638-E-008-001-MY2(Shackleton Program Grant)).
文摘Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow<1200 vehicles per hour.This study also classified rainfall events into four types depending on its characteristic.Wet deposition from varied rainfall types significantly affects air quality,with TypeⅠrainfall events(long-duration heavy rain)having the most pronounced impact.An LSTM model incorporating GAM and Bayesian network outcomes yields excellent performance,achieving correlation R^(2)>0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO_(2),and NO_(x);and O_(3),PM_(10),and PM_(2.5),respectively.
基金supported by the National Key Research and Development Program of China(Nos.2023YFC3707301 and 2023YFC3705400)the Fundamental Research Funds for the Central Universities(Nos.ZB23003425 and 63211075)。
文摘To investigate the response of Roadside Monitoring Stations(RSs)to traffic-related air pollution,traffic and pollutant characteristics,influencing factors,and potential source characterization in Tianjin,China were determined based on roadside monitoring of real-world data conducted at RSs in 2022.The diurnal variation trend of pollutants at RSs was consistent with that at the National Monitoring Station(NM),with notably higher pollutant fluctuations during the morning and evening peak traffic times at RSs,where the average diurnal concentration was 41.46%higher than that at the NM.The generalized additive model(GAM)for nitrogen oxides(NO_(x))and carbon monoxide(CO),responding to themultiple influencing factors,performed well at RSs,with deviance explained by 86.6%and 61.4%,respectively.The synergistic effects of wind direction and speed contributed to most of the variations in NO_(x) and CO,which were 14.74%and 12.87%,respectively.Pollutant concentrations were highest under windless conditions,with pollutants originating primarily from local vehicle emissions.The model results indicated that medium-duty truck(MDT)traffic flow predominantly contributed to the variability in NO_(x) emissions,whereas passenger car(PC)traffic flow was the primary source of CO emissions from traffic variables.MDTs should be the focus of urban NO_(x) traffic emissions control.Potential-source analysis validated the results obtained from the GAM,and both analyses showed that RSs can better characterize traffic-related air pollutants.Furthermore,more stringent emission standards have effectively mitigated the release of pollutants from motor vehicles and contributed to the modernization of vehicle fleet composition,effectively decreasing CO concentrations.
基金supported by the National Natural Science Foundation of China(62373224,62333013,U23A20327)the Natural Science Foundation of Shandong Province(ZR2024JQ021)
文摘Dear Editor,Health management is essential to ensure battery performance and safety, while data-driven learning system is a promising solution to enable efficient state of health(SoH) estimation of lithium-ion(Liion) batteries. However, the time-consuming signal data acquisition and the lack of interpretability of model still hinder its efficient deployment. Motivated by this, this letter proposes a novel and interpretable data-driven learning strategy through combining the benefits of explainable AI and non-destructive ultrasonic detection for battery SoH estimation. Specifically, after equipping battery with advanced ultrasonic sensor to promise fast real-time ultrasonic signal measurement, an interpretable data-driven learning strategy named generalized additive neural decision ensemble(GANDE) is designed to rapidly estimate battery SoH and explain the effects of the involved ultrasonic features of interest.
基金funded by the Yinshanbeilu Grassland Eco-hydrology National Observation and Research Station,China Institute of Water Resources and Hydropower Research(YSS202105)the National Natural Science Foundation of China(52269005)+3 种基金the Inner Mongolia Science and Technology Plan Project(2022YFSH0105)the Central Guidance for Local Science and Technology Development Fund Projects(2024ZY0002)the Inner Mongolia Autonomous Region University Youth Science and Technology Talent Project(NJYT 22037)the Inner Mongolia Agricultural University Young Teachers'Scientific Research Ability Improvement Project(BR220104).
文摘Recent years have witnessed increasingly frequent extreme precipitation events,especially in desert steppes in the semi-arid and arid transition zone.Focusing on a desert steppe in western-central Inner Mongolia Autonomous Region,China,this study aimed to determine the principle time-varying pattern of extreme precipitation and its dominant climate forcings during the period 1988-2017.Based on the generalized additive models for location,scale,and shape(GAMLSS)modeling framework,we developed the best time-dependent models for the extreme precipitation series at nine stations,as well as the optimized non-stationary models with large-scale climate indices(including the North Atlantic Oscillation(NAO),Atlantic Multidecadal Oscillation(AMO),Southern Oscillation(SO),Pacific Decadal Oscillation(PDO),Arctic Oscillation(AO),and North Pacific Oscillation(NPO))as covariates.The results indicated that extreme precipitation remained stationary at more than half of the stations(Hailisu,Wuyuan,Dengkou,Hanggin Rear Banner,Urad Front Banner,and Yikewusu),while linear and non-linear time-varying patterns were quantitatively identified at the other stations(Urad Middle Banner,Linhe,and Wuhai).These non-stationary behaviors of extreme precipitation were mainly reflected in the mean value of extreme precipitation.The optimized non-stationary models performed best,indicating the significant influences of large-scale climate indices on extreme precipitation.In particular,the NAO,NPO,SO,and AMO remained as covariates and significantly influenced the variations in the extreme precipitation regime.Our findings have important reference significance for gaining an in-depth understanding of the driving mechanism of the non-stationary behavior of extreme precipitation and enable advanced predictions of rainstorm risks.
基金Under the auspices of National Natural Science Foundation of China(No.41001363)
文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.
基金Project(51774219)supported by the National Natural Science Foundation of China
文摘This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.
基金supported by National Natural Science Foundation of China (51774219)Science and Technology Research Program of Hubei Ministry of Education(D20161103)Youth Science and technology Program of Wuhan(2016070204010099)
文摘A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.
基金Supported by the National Natural Science Foundation of China （61273131） 111 Project （B12018）+1 种基金 the Innovation Project of Graduate in Jiangsu Province （CXZZ12_0741） the Fundamental Research Funds for the Central Universities （JUDCF12034）
文摘Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.
基金Under the auspices of the Project of National Natural Science Foundation of China ( No. 41001363)Autonomous Project of State Key Laboratory of Resources and Environmental Information System,Geo-information Tupu Theory and Virtual Geoscience
文摘There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.
文摘In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.
文摘Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments.As an example,Iranian montane ranges are highly diverse,particularly in the Irano-Turanian phytogeographical region.Understanding plant diversity patterns with increasing elevation is of high significance,not least for conservation planning.We studied the pattern of species richness,Shannon diversity,endemic richness,endemics ratio,and richness of life forms along a 3900 m elevational transect in Mount Palvar,overlooking the Lut Desert in Southeast Iran.We also analyzed the effect of environmental variables on species turnover along the vertical gradient.A total of 120 vegetation plots(10 m×10 m)were sampled along the elevational transect containing species and environmental data.To discover plant diversity pattern along the elevational gradient,generalized additive model(GAM)was used.Non-metric multidimensional scaling(NMDS)was applied for illustrating the correlation between species composition and environmental variables.We found hump-shaped pattern for species richness,Shannon diversity,endemic richness,and species richness of different life forms,but a monotonic increasing pattern for ratio of endemic species from low to high elevations.Our study confirms the humped pattern of species richness peaking at intermediate elevations along a complete elevational gradient in a semi-arid mountain.The monotonic increase of endemics ratio with elevation in our area as a case study is consistent with global increase of endemism with elevation.According to our results,temperature and precipitation are two important climatic variables that drive elevational plant diversity,particularly in seasonally dry areas.Our study suggests that effective conservation and management are needed for this low latitude mountain area along with calling for long-term monitoring for species redistribution.
基金funded by National Science Centre,Poland under the project"Assessment of the impact of weather conditions on forest health status and forest disturbances at regional and national scale based on the integration of ground and space-based remote sensing datasets"(project no.2021/41/B/ST10/)Data collection and research was also supported by the project no.EZ.271.3.19.2021"Modele ryzyka zamierania drzewostanow glownych gatunkow lasotworczych Polski"funded by the General Directorate of State Forests in Poland。
文摘Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.
基金The National Forestry Commission of Mexico and The Mexican National Council for Science and Technology(CONAFOR-CONACYT-115900)。
文摘Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.
文摘Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.
基金funded by the Bavarian Ministry for Food,Agriculture and Forestry (Grant No.F053)support by the Open Access Publication Funds/transformative agreements of the Gottingen University
文摘In Central Europe,anthropogenic coniferous monocultures have been subject to conversion to more diverse mixed forests since the 1990s,however,they are still abundant across many forest landscapes.Artificial and natural tree regeneration both play a key role during conversion by determining the species composition and structure of the future forests.Many abiotic and biotic factors can potentially influence the regeneration process and its specific combinations or interactions may be different among tree species and its developmental stages.Here,we aimed to identify and quantify the effect of the most important drivers on the density of the most abundant regenerating tree species(i.e.,Norway spruce and European beech),as well as on species and structural diversity of the tree regeneration.We studied tree regeneration in four former monospecific coniferous stand types(i.e.,Norway spruce,Scots pine,European larch,and Douglas fir)in Southwest Germany that have been under conversion to mixed forests since the 1990s.We sampled tree regeneration in four growth height classes together with a variety of potentially influencing factors on 108 sampling plots and applied multivariate analyses.We identified light availability in the understorey,stand structural attributes,browsing pressure,and diaspore source abundance as the most important factors for the density and diversity of tree regeneration.Particularly,we revealed speciesspecific differences in drivers of regeneration density.While spruce profited from increasing light availability and decreasing stand basal area,beech benefited either from a minor reduction or more strikingly from an increase in overstorey density.Increasing diaspore source abundance positively and a high browsing pressure negatively affected both species equally.Our results suggest that humus and topsoil properties were modified during conversion,probably due to changes in tree species composition and silvicultural activities.The species and structural diversity of the tree regeneration benefitted from increasing light availability,decreasing stand basal area,and a low to moderate browsing pressure.We conclude that forest managers may carefully equilibrate among the regulation of overstorey cover,stand basal area,and browsing pressure to fulfil the objectives of forest conversion,i.e.,establishing and safeguarding a diverse tree regeneration to promote the development of mature mixed forests in the future.
文摘This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that em-ployed Frequentist and Bayesian Linear Mixed Model,continent-specific ran-dom slopes for exchange rate were incorporated to assess the variability in the rate at which changes in exchange rate influence inflation.Fixed effects cap-tured the overall impact of the predictors,while random effects accounted for regional differences.Results consistently showed that the monetary policy rate significantly affects inflation,whereas the exchange rate does not.Strong evi-dence supported variation in baseline inflation across continents(random in-tercepts),but findings on random slope variability were mixed,suggesting modest and model-dependent heterogeneity.Bayesian models offered a slightly better fit and predictive accuracy.These findings underscore the central role of monetary policy in inflation control,while exchange rate effects remain context-dependent.These results highlight the importance of accounting for regional heterogeneity when modelling global inflation dynamics.Policymak-ers should tailor inflation strategies to regional contexts and prioritize robust monetary policy tools over exchange rate management.These findings are as-sociational,not causal and future research should adopt a credible causal iden-tification strategy to establish causal relationships.
基金Supported by the National High Technology Research and Development Program of China (863 Program, No. 2003AA607030)National Key Technology Research and Development Program (No. 2006BAD09A05)
文摘The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature （SST）, chlorophyll concentration （Chl-α） and sea surface height （SSH） from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10℃-22℃ and the favorite temperature is between 15℃-17℃. The Chl-α concentration is 0.1-0.6 mg/m^3. When Chl-α concentration changes to 0.12-0.14 mg/m^3, the probability of forming fishing ground becomes very high. In most fishing grounds, the SSH is higher than the mean SSH. The generalized additive model （GAM） was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October-November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude.
