Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological...Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.展开更多
To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a compar...To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a comparison model.This method enhances the precision of system calibration by establishing a dataset that delineates the relationship between secondary mirror misalignment and wavefront aberration,subsequently inferring the misalignment from interferometric detection results during the calibration process.For the 2-m ring telescope,we develop a detection model using five local sub-apertures,enabling a root-mean-square detection accuracy of 0:0225λ(λ=632:8 nm)for full-aperture wavefront aberration.The calibration results for the 2-m Ring Solar Telescope system indicate that the root-mean-square value of sub-aperture wavefront aberration reaches 0.104λ,and the root-mean-square value of spliced full-aperture measurement yields reaches 0.112λ.This method offers a novel approach for calibrating small width-toradius ratio telescope systems and can be applied to the calibration of other irregular-aperture optical systems.展开更多
The cross sections of fragments produced in the 140 A MeV58,64Ni＋9 Be projectile fragmentation reactions are calculated by using the antisymmetrized molecular dynamics （AMD） model, the modified statistical abrasion...The cross sections of fragments produced in the 140 A MeV58,64Ni＋9 Be projectile fragmentation reactions are calculated by using the antisymmetrized molecular dynamics （AMD） model, the modified statistical abrasion- ablation （SAA） model, and the empirical EPAX2/EPAX3 formulae. The Gogny-gO interaction is taken as the effective nucleon-nucleon interaction in the AMD calculation, and the decays of fragments obtained from the AMD results are calculated by using the GEMINI code. The calculated cross sections of fragments are compared.展开更多
A model comparison based software testing method （MCST） is proposed. In this method, the requirements aria programs or software under test are transformed into the ones in the same form, and described by the same mo...A model comparison based software testing method （MCST） is proposed. In this method, the requirements aria programs or software under test are transformed into the ones in the same form, and described by the same model describe language （MDL）. Then, the requirements are transformed into a specification model and the programs into an implementation model. Thus, the elements and structures of the two models are compared, and the differences between them are obtained. Based on the diffrences, a test suite is generated. Different MDLs can be chosen for the software under test. The usages of two classical MDLs in MCST, the equivalence classes model and the extended finite state machine （EFSM） model, are described with example applications. The results show that the test suites generated by MCST are more efficient and smaller than some other testing methods, such as the pathcoverage testing method, the object state diagram testing method, etc.展开更多
The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried o...The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.展开更多
Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the co...Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.展开更多
The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model...The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.展开更多
Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feed...Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source. Thirty-two cows were randomly assigned to one of two total mixed ration groups： a ration containing a mixed forage （MF） of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover （CS） as unique forage source. The actual DMI was greater in MF group than in CS group （P=0.064）. The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups （1.09 kg d-1 vs. 1.28 kg d-1） and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet （0.89 vs. 0.87）. Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values （P〈0.001）. The DMI variation in MF was caused by week of lactation （55.6%）, milk yield （13.9%）, milk fat percentage （7.1%） and dietary neutral detergent fiber （13.3%）, while the variation in CS was caused by week of lactation （50.9%）, live body weight （28.2%）, milk yield （8.4%）, milk fat percentage （5.2%） and dietary neutral detergent fibre （3.8%）. In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.展开更多
Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age...Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphenoid ridge.Results： All the patents received total resection of meningiomas with the help of this new tool, and the critical brain vessels and nerves were preserved during operations. All the patients recovered well after operation.Conclusions： This new CUSA Excel ultrasonic aspiration system has the advantage of preserving vital brain arteries and cranial nerves during skull base meningioma resection, which is very important for skull base tumor operations. This key step would ensure a well prognosis for patients. We hope the neurosurgeons would benefit from this kind of technique.Background： The purposes of this study were to explore the effects of high mobility group protein box 1（HMGB1） gene on the growth, proliferation, apoptosis, invasion, and metastasis of glioma cells, with an attempt to provide potential therapeutic targets for the treatment of glioma. Methods： The expressions of HMGB1 in glioma cells（U251, U-87 MG and LN-18） and one control cell line（SVG p12） were detected by real time PCR and Western blotting, respectively. Then, the effects of HMGB1 on the biological behaviors of glioma cells were detected： the expression of HMGB1 in human glioma cell lines U251 and U-87 MG were suppressed using RNAi technique, then the influences of HMGB1 on the viability, cycle, apoptosis, and invasion abilities of U251 and U-87 MG cells were analyzed using in a Transwell invasion chamber. Also, the effects of HMGB1 on the expressions of cyclin D1, Bax, Bcl-2, and MMP 9 were detected. Results： As shown by real-time PCR and Western blotting, the expression of HMGB1 significantly increased in glioma cells（U251, U-87 MG, and LN-18） in comparison with the control cell line（SVG p12）; the vitality, proliferation and invasive capabilities of U251 and U-87 MG cells in the HMGB1 siR NA-transfected group were significantly lower than those in the blank control group and negative control（NC） siR NA group（P〈0.05） but showed no significant difference between the blank control group and NC siR NA group. The percentage of apoptotic U251 and U-87 MG cells was significantly higher in the HMGB1 siR NA-transfected group than in the blank control group and NC siR NA group（P〈0.05） but was similar between the latter two groups. The HMGB1 siR NA-transfected group had significantly lower expression levels of Cyclin D1, Bcl-2, and MMP-9 protein in U251 and U-87 MG cells and significantly higher expression of Bax protein than in the blank control group and NC siR NA group（P〈0.05）; the expression profiles of cyclin D1, Bax, Bcl-2, and MMP 9 showed no significant change in both blank control group and NC siR NA group. Conclusions： HMGB1 gene may promote the proliferation and migration of glioma cells and suppress its effects of apoptosis. Inhibition of the expression of HMGB1 gene can suppress the proliferation and migration of glioma cells and promote their apoptosis. Our observations provided a new target for intervention and treatment of glioma.展开更多
Potential evapotranspiration(EPET)is usually calculated by empirical methods from surface meteorological variables,such as temperature,radiation and wind speed.The in-situ measured pan evaporation(ETpan)can also be us...Potential evapotranspiration(EPET)is usually calculated by empirical methods from surface meteorological variables,such as temperature,radiation and wind speed.The in-situ measured pan evaporation(ETpan)can also be used as a proxy for EPET.In this study,EPET values computed from ten models are compared with observed ETpan data in ten Chinese river basins for the period 1961−2013.The daily observed meteorological variables at 2267 stations are used as the input to those models,and a ranking scheme is applied to rank the statistical quantities(ratio of standard deviations,correlation coefficient,and ratio of trends)between ETpan and modeled EPET in different river basins.There are large deviations between the modeled EPET and the ETpan in both the magnitude and the annual trend at most stations.In eight of the basins(except for Southeast and Southwest China),ETpan shows decreasing trends with magnitudes ranging between−0.01 mm d−1 yr−1 and−0.03 mm d−1 yr−1,while the decreasing trends in modeled EPET are less than−0.01 mm d−1 yr−1.Inter comparisons among different models in different river basins suggest that PETHam1 is the best model in the Pearl River basin,PETHam2 outperforms other models in the Huaihe River,Yangtze River and Yellow River basins,and PETFAO is the best model for the remaining basins.Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing EPET in most basins except in Southeast and Southwest China.The increasing EPET trend in Southeast China is mainly attributed to the reduced relative humidity.展开更多
The monsoon intraseasonal oscillation(MISO)is the dominant variability over the Indian Ocean during the Indian summer monsoon(ISM)season and is characterized by pronounced northward propagation.Previous studies have s...The monsoon intraseasonal oscillation(MISO)is the dominant variability over the Indian Ocean during the Indian summer monsoon(ISM)season and is characterized by pronounced northward propagation.Previous studies have shown that general circulation models(GCMs)still have difficulty in simulating the northwardpropagating MISO,and that the role of air-sea interaction in MISO is unclear.In this study,14 atmosphere-ocean coupled GCMs(CGCMs)and the corresponding atmosphere-only GCMs(AGCMs)are selected from Phase 6 of the Coupled Model Intercomparison Project(CMIP6)to assess their performance in reproducing MISO and the associated vortex tilting mechanism.The results show that both CGCMs and AGCMs are able to well simulate the significant relationship between MISO and vortex tilting.However,80%of CGCMs show better simulation skills for MISO than AGCMs in CMIP6.In AGCMs,the poor model fidelity in MISO is due to the failure simulation of vortex tilting.Moreover,it is found that failure to simulate the downward motion to the north of convection is responsible for the poor simulation of vortex tilting in AGCMs.In addition,it is observed that there is a significant relationship between the simulated sea surface temperature gradient and simulated vertical velocity shear in the meridional direction.These findings indicate that air-sea interaction may play a vital role in simulating vertical motions in tilting and MISO processes.This work offers us a specific target to improve the MISO simulation and further studies are needed to elucidate the physical processes of this air-sea interaction coupling with vortex tilting.展开更多
In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean...In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean observation）, r （correlation）, and R2 （determination coefficient）, are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap〈5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects：（1） calculating da and dap; （2） calculating the RMSE or Sd; （3） calculating r and R2, at the same time, plotting 1：1 diagram.展开更多
Jerome Model and Horace Model are the two influential translation models in the translation field. This article tries to find the similarities and differences between these two models.
Compound Poisson risk model has been simulated. It has started with exponential claim sizes. The simulations have checked for infinite ruin probabilities. An appropriate time window has been chosen to estimate and com...Compound Poisson risk model has been simulated. It has started with exponential claim sizes. The simulations have checked for infinite ruin probabilities. An appropriate time window has been chosen to estimate and compare ruin probabilities. The infinite ruin probabilities of two-compound Poisson risk process have estimated and compared them with standard theoretical results.展开更多
Projections of future urban land change are essential for a range of sustainability assessments,including those related to biodiversity loss,carbon emissions,and agricultural land conversion.However,to what extent and...Projections of future urban land change are essential for a range of sustainability assessments,including those related to biodiversity loss,carbon emissions,and agricultural land conversion.However,to what extent and where current projections agree or disagree remains unknown.Here,we systematically compare existing global projections that are consistent with the Shared Socioeconomic Pathways.We find that the total global urban land area is expected to increase by 112%between 2020 and 2100(averaged across all projections),with a coefficient of variation of 0.81.This variation is mostly caused by the selection of the underlying drivers that are included in the different models.Regionally,the highest average growth rates are found in sub-Saharan Africa(+679%to+730%),while this region also has the highest variation across projections(coefficient of variation ranging from 2.02 to 2.18).When ranking scenarios within a study from the highest to the lowest projected increase in urban land,rankings are relatively similar for regions in the Global North,but not for regions in the Global South.The large disagreement across projections can lead to high uncertainties in assessments of future urban land change impacts,which can undermine the effectiveness of long-term planning,policymaking,and resource management decisions.展开更多
基金Project(2024JJ2074) supported by the Natural Science Foundation of Hunan Province,ChinaProject(22376221) supported by the National Natural Science Foundation of ChinaProject(2023QNRC001) supported by the Young Elite Scientists Sponsorship Program by CAST,China。
文摘Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.
基金supported by the Jiangsu Provincial Key Research and Development Program(BE2022072)the National Natural Science Foundation of China(12141304)the Natural Science Foundation of Jiangsu Province(BK20231134).
文摘To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a comparison model.This method enhances the precision of system calibration by establishing a dataset that delineates the relationship between secondary mirror misalignment and wavefront aberration,subsequently inferring the misalignment from interferometric detection results during the calibration process.For the 2-m ring telescope,we develop a detection model using five local sub-apertures,enabling a root-mean-square detection accuracy of 0:0225λ(λ=632:8 nm)for full-aperture wavefront aberration.The calibration results for the 2-m Ring Solar Telescope system indicate that the root-mean-square value of sub-aperture wavefront aberration reaches 0.104λ,and the root-mean-square value of spliced full-aperture measurement yields reaches 0.112λ.This method offers a novel approach for calibrating small width-toradius ratio telescope systems and can be applied to the calibration of other irregular-aperture optical systems.
基金Supported by the Program for Science and Technology Innovation Talents in Universities of Henan Province under Grant No13HASTIT046
文摘The cross sections of fragments produced in the 140 A MeV58,64Ni＋9 Be projectile fragmentation reactions are calculated by using the antisymmetrized molecular dynamics （AMD） model, the modified statistical abrasion- ablation （SAA） model, and the empirical EPAX2/EPAX3 formulae. The Gogny-gO interaction is taken as the effective nucleon-nucleon interaction in the AMD calculation, and the decays of fragments obtained from the AMD results are calculated by using the GEMINI code. The calculated cross sections of fragments are compared.
基金The National Natural Science Foundationof Hubei Province (No.2005ABA266)
文摘A model comparison based software testing method （MCST） is proposed. In this method, the requirements aria programs or software under test are transformed into the ones in the same form, and described by the same model describe language （MDL）. Then, the requirements are transformed into a specification model and the programs into an implementation model. Thus, the elements and structures of the two models are compared, and the differences between them are obtained. Based on the diffrences, a test suite is generated. Different MDLs can be chosen for the software under test. The usages of two classical MDLs in MCST, the equivalence classes model and the extended finite state machine （EFSM） model, are described with example applications. The results show that the test suites generated by MCST are more efficient and smaller than some other testing methods, such as the pathcoverage testing method, the object state diagram testing method, etc.
基金funding from the Engineering and Physical Sciences Research Council(EPSRC grant number EP/W006790/1)the Medical Research Council(MRC grant number MR/N010760/1)the National Institute for Health Research(NIHR grant number NIHR200892).
文摘The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.
基金financially supported by the Natural Science Foundation of Hunan Province,China(No.2024JJ2074)the National Natural Science Foundation of China(No.22376221)the Young Elite Scientists Sponsorship Program by CAST,China(No.2023QNRC001).
文摘Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
基金Supported by the National Natural Science Foundation of China(41205126)the Discipline Construction and Macroscopic Agricultural Research Project of Anhui Academy of Agricultural Sciences(13A1424)+2 种基金the Fund for Youth Innovation of Anhui Academy of Agricultural Sciences(14B1460)the Innovative Research Team for Agricultural Disaster Risk Analysis in Anhui ProvinceAnhui Academy of Agricultural Sciences(14C1409)~~
文摘The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.
基金financially supported by the National Natural Science Foundation of China(31572435)the National Key Research and Development Plan(2016YFD0700205,2016YFD0700201)
文摘Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source. Thirty-two cows were randomly assigned to one of two total mixed ration groups： a ration containing a mixed forage （MF） of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover （CS） as unique forage source. The actual DMI was greater in MF group than in CS group （P=0.064）. The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups （1.09 kg d-1 vs. 1.28 kg d-1） and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet （0.89 vs. 0.87）. Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values （P〈0.001）. The DMI variation in MF was caused by week of lactation （55.6%）, milk yield （13.9%）, milk fat percentage （7.1%） and dietary neutral detergent fiber （13.3%）, while the variation in CS was caused by week of lactation （50.9%）, live body weight （28.2%）, milk yield （8.4%）, milk fat percentage （5.2%） and dietary neutral detergent fibre （3.8%）. In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.
基金granted by the initiative research scheme for college student, Guangdong, China (No. 1212110046)
文摘Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphenoid ridge.Results： All the patents received total resection of meningiomas with the help of this new tool, and the critical brain vessels and nerves were preserved during operations. All the patients recovered well after operation.Conclusions： This new CUSA Excel ultrasonic aspiration system has the advantage of preserving vital brain arteries and cranial nerves during skull base meningioma resection, which is very important for skull base tumor operations. This key step would ensure a well prognosis for patients. We hope the neurosurgeons would benefit from this kind of technique.Background： The purposes of this study were to explore the effects of high mobility group protein box 1（HMGB1） gene on the growth, proliferation, apoptosis, invasion, and metastasis of glioma cells, with an attempt to provide potential therapeutic targets for the treatment of glioma. Methods： The expressions of HMGB1 in glioma cells（U251, U-87 MG and LN-18） and one control cell line（SVG p12） were detected by real time PCR and Western blotting, respectively. Then, the effects of HMGB1 on the biological behaviors of glioma cells were detected： the expression of HMGB1 in human glioma cell lines U251 and U-87 MG were suppressed using RNAi technique, then the influences of HMGB1 on the viability, cycle, apoptosis, and invasion abilities of U251 and U-87 MG cells were analyzed using in a Transwell invasion chamber. Also, the effects of HMGB1 on the expressions of cyclin D1, Bax, Bcl-2, and MMP 9 were detected. Results： As shown by real-time PCR and Western blotting, the expression of HMGB1 significantly increased in glioma cells（U251, U-87 MG, and LN-18） in comparison with the control cell line（SVG p12）; the vitality, proliferation and invasive capabilities of U251 and U-87 MG cells in the HMGB1 siR NA-transfected group were significantly lower than those in the blank control group and negative control（NC） siR NA group（P〈0.05） but showed no significant difference between the blank control group and NC siR NA group. The percentage of apoptotic U251 and U-87 MG cells was significantly higher in the HMGB1 siR NA-transfected group than in the blank control group and NC siR NA group（P〈0.05） but was similar between the latter two groups. The HMGB1 siR NA-transfected group had significantly lower expression levels of Cyclin D1, Bcl-2, and MMP-9 protein in U251 and U-87 MG cells and significantly higher expression of Bax protein than in the blank control group and NC siR NA group（P〈0.05）; the expression profiles of cyclin D1, Bax, Bcl-2, and MMP 9 showed no significant change in both blank control group and NC siR NA group. Conclusions： HMGB1 gene may promote the proliferation and migration of glioma cells and suppress its effects of apoptosis. Inhibition of the expression of HMGB1 gene can suppress the proliferation and migration of glioma cells and promote their apoptosis. Our observations provided a new target for intervention and treatment of glioma.
基金supported by the National Natural Science Foundation of China(Grant No.41875106)the National Key R&D Program of China(Grant No.2016YFA0602401)。
文摘Potential evapotranspiration(EPET)is usually calculated by empirical methods from surface meteorological variables,such as temperature,radiation and wind speed.The in-situ measured pan evaporation(ETpan)can also be used as a proxy for EPET.In this study,EPET values computed from ten models are compared with observed ETpan data in ten Chinese river basins for the period 1961−2013.The daily observed meteorological variables at 2267 stations are used as the input to those models,and a ranking scheme is applied to rank the statistical quantities(ratio of standard deviations,correlation coefficient,and ratio of trends)between ETpan and modeled EPET in different river basins.There are large deviations between the modeled EPET and the ETpan in both the magnitude and the annual trend at most stations.In eight of the basins(except for Southeast and Southwest China),ETpan shows decreasing trends with magnitudes ranging between−0.01 mm d−1 yr−1 and−0.03 mm d−1 yr−1,while the decreasing trends in modeled EPET are less than−0.01 mm d−1 yr−1.Inter comparisons among different models in different river basins suggest that PETHam1 is the best model in the Pearl River basin,PETHam2 outperforms other models in the Huaihe River,Yangtze River and Yellow River basins,and PETFAO is the best model for the remaining basins.Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing EPET in most basins except in Southeast and Southwest China.The increasing EPET trend in Southeast China is mainly attributed to the reduced relative humidity.
基金The Zhejiang Provincial Natural Science Foundation of China under contract No.LR19D060001the Scientific Research Fund of the Second Institute of Oceanography,Ministry of Natural Resources,under contract No.JB2206+1 种基金the China Postdoctoral Science Foundation under contract Nos 2022M711010 and 2021M703792the National Natural Science Foundation of China under contract No.42106003。
文摘The monsoon intraseasonal oscillation(MISO)is the dominant variability over the Indian Ocean during the Indian summer monsoon(ISM)season and is characterized by pronounced northward propagation.Previous studies have shown that general circulation models(GCMs)still have difficulty in simulating the northwardpropagating MISO,and that the role of air-sea interaction in MISO is unclear.In this study,14 atmosphere-ocean coupled GCMs(CGCMs)and the corresponding atmosphere-only GCMs(AGCMs)are selected from Phase 6 of the Coupled Model Intercomparison Project(CMIP6)to assess their performance in reproducing MISO and the associated vortex tilting mechanism.The results show that both CGCMs and AGCMs are able to well simulate the significant relationship between MISO and vortex tilting.However,80%of CGCMs show better simulation skills for MISO than AGCMs in CMIP6.In AGCMs,the poor model fidelity in MISO is due to the failure simulation of vortex tilting.Moreover,it is found that failure to simulate the downward motion to the north of convection is responsible for the poor simulation of vortex tilting in AGCMs.In addition,it is observed that there is a significant relationship between the simulated sea surface temperature gradient and simulated vertical velocity shear in the meridional direction.These findings indicate that air-sea interaction may play a vital role in simulating vertical motions in tilting and MISO processes.This work offers us a specific target to improve the MISO simulation and further studies are needed to elucidate the physical processes of this air-sea interaction coupling with vortex tilting.
基金supported by the National High-Tech R&D Program (2006AA10Z230,2006AA10Z219-1)the National Natural Science Foundation of China (31171455)+3 种基金the Jiangsu Province Agricultural Scientific Technology Innovation Fund, China (CX(10)221, CX (11)2042)the Agricultural Scientific Technology Support Program, Jiangsu Province, China (BE2008397,BE2011342)the No-Profit Industry (Meteorology) Research Program, China (GYHY201006027, GYHY201106027)the Jiangsu Government Scholarship for Overseas Studies, China
文摘In this paper, the many indices used in validation of crop models, such as RMSE （root mean square errors）, Sd （standard error of absolute difference）, da （mean absolute difference）, dap （ratio of da to the mean observation）, r （correlation）, and R2 （determination coefficient）, are compared for the same rice architectural parameter model, and their advantages and disadvantages are analyzed. A new index for validation of crop models, dap between the observed and the simulated values, is proposed, with dap〈5% as the suggested standard for precision of crop models. The different kinds of validation methods in crop models should be combined in the following aspects：（1） calculating da and dap; （2） calculating the RMSE or Sd; （3） calculating r and R2, at the same time, plotting 1：1 diagram.
文摘Jerome Model and Horace Model are the two influential translation models in the translation field. This article tries to find the similarities and differences between these two models.
文摘Compound Poisson risk model has been simulated. It has started with exponential claim sizes. The simulations have checked for infinite ruin probabilities. An appropriate time window has been chosen to estimate and compare ruin probabilities. The infinite ruin probabilities of two-compound Poisson risk process have estimated and compared them with standard theoretical results.
基金supported by the Netherlands Organization for Scientific Research NWO in the form of a VIDI grant(Grant No.VI.Vidi.198.008).
文摘Projections of future urban land change are essential for a range of sustainability assessments,including those related to biodiversity loss,carbon emissions,and agricultural land conversion.However,to what extent and where current projections agree or disagree remains unknown.Here,we systematically compare existing global projections that are consistent with the Shared Socioeconomic Pathways.We find that the total global urban land area is expected to increase by 112%between 2020 and 2100(averaged across all projections),with a coefficient of variation of 0.81.This variation is mostly caused by the selection of the underlying drivers that are included in the different models.Regionally,the highest average growth rates are found in sub-Saharan Africa(+679%to+730%),while this region also has the highest variation across projections(coefficient of variation ranging from 2.02 to 2.18).When ranking scenarios within a study from the highest to the lowest projected increase in urban land,rankings are relatively similar for regions in the Global North,but not for regions in the Global South.The large disagreement across projections can lead to high uncertainties in assessments of future urban land change impacts,which can undermine the effectiveness of long-term planning,policymaking,and resource management decisions.
