Flow records for stations in the Casamance basin are incomplete. Several gaps were noted over the 1980-2021 study period, making this study tedious. The aim of this study is to assess the potential impact of climate c...Flow records for stations in the Casamance basin are incomplete. Several gaps were noted over the 1980-2021 study period, making this study tedious. The aim of this study is to assess the potential impact of climate change on the flow of the Casamance watershed at Kolda. To this end, hydrological series are simulated and then extended using the GR2M rainfall-runoff model, with a monthly time step. Projected climate data are derived from a multi-model ensemble under scenarios SSP2-4.5 (scenario with additional radiative forcing of 4.5 W/m<sup>2</sup> by 2099) and SSP5-8.5 (scenario with additional radiative forcing of 8.5 W/m<sup>2</sup> by 2099). An analysis of the homogeneity of the rainfall data series from the Kolda station was carried out using KhronoStat software. The Casamance watershed was then delimited using ArcGIS to determine the morphometric parameters of the basin, which will be decisive for the rest of the work. Next, monthly evapotranspiration was calculated using the formula proposed by Oudin et al. This, together with rainfall and runoff, forms the input data for the model. The GR2M model was then calibrated and cross-validated using various simulations to assess its performance and robustness in the Casamance watershed. The version of the model with the calibrated parameters will make it possible to extend Casamance river flows to 2099. This simulation of future flows with GR2M shows a decrease in the flow of the Casamance at Kolda with the two scenarios SSP2-4.5 and SSP5-8.5 during the rainy period, and almost zero flows during the dry season from the period 2040-2059.展开更多
Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference backgro...Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.展开更多
A quantum model based on solutions to the Schrodinger-Poisson equations is developed to investigate the device behavior related togate tunneling current for nanoscale MOSFETs with high-k gate stacks. This model can mo...A quantum model based on solutions to the Schrodinger-Poisson equations is developed to investigate the device behavior related togate tunneling current for nanoscale MOSFETs with high-k gate stacks. This model can model various MOS device structures with combinations of high-k dielectric materials and multilayer gate stacks,revealing quantum effects on the device performance. Comparisons are made for gate current behavior between nMOSFET and pMOSFET high- k gate stack structures. The results presented are consistent with experimental data, whereas a new finding for an optimum nitrogen content in HfSiON gate dielectric requires further experimental verifications.展开更多
In order to effectively predict the fracture of AA7075-T6 sheet, the forming limit curves of AA7075-T6 high-strength sheet were drawn according to Morciniak Kuczyski (M K) model and Lou Huh criterion, respectively. Th...In order to effectively predict the fracture of AA7075-T6 sheet, the forming limit curves of AA7075-T6 high-strength sheet were drawn according to Morciniak Kuczyski (M K) model and Lou Huh criterion, respectively. The errors between the predicted values of the two theoretical prediction models and experimental values were calculated by error analysis. The forming limit curves were verified by the punch stretch test to evaluate the prediction accuracy of M K model and Lou Huh criterion. The error analysis results show that the mean error of Lou Huh criterion with the optimal parameters for all tensile specimens is 25.04%, while the mean error of M K model for all tensile specimens is 74.24%. The prediction accuracy of Lou Huh criterion in predicting the fracture of AA7075-T6 sheet is higher. The punch stretch test results show that the forming limit curve drawn by Lou Huh criterion can effectively predict the fracture of AA7075-T6 sheet, but the prediction accuracy of M K model is relatively poor.展开更多
Objective To establish an effective assay to access the effects of natural products on cathepsin K for screening antiosteoporosis drugs. Methods To obtain the purified cathepsin K, we cloned the target fragment fro...Objective To establish an effective assay to access the effects of natural products on cathepsin K for screening antiosteoporosis drugs. Methods To obtain the purified cathepsin K, we cloned the target fragment from the mRNA of human osteosacoma cell line MG63 and demonstrated its correctness through DNA sequencing. Cathepsin K was expressed in a high amount in E.coli after IPTG induction, and was purified to near homogenetity through resolution and column purification. The specificity of the protein was shown by Western blotting experiment. The biological activity of the components in the fermentation broth was assayed by their inhibitory effects on cathepsin K and its analog papain. Results With the inhibition of papain activity as a screen index, the fermentation samples of one thousand strains of fungi were tested and 9 strains among them showed strong inhibitory effects. The crude products of the fermentation broth were tested for their specific inhibitory effects on the purified human cathepsin K, the product of fungi 2358 shows the highest specificity against cathepsin K. Conclusions The compounds isolated from fungi 2358 show the highest biological activity and are worth further structure elucidation and function characterization.展开更多
The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of ...The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of N, P2O5, K2O in cotton leaves, stems, squares and bolls decreased obviously with the time over the whole growth duration and the falling extent was greater in high-yield cotton than in CK. Contents of N in leaves, squares and bolls, in particular in the leaves of fruit-bearing shoot was higher in high-yield cotton than in CK. Contents of P2O5 in squares and bolls and that of K2O in stems were higher in high-yield cotton than in CK during the whole growing period. The accumulations of N, P2O5 and K2O in the cotton plants could be described with a logistic curve equation. There was the fastest nutrient uptake at about 90 d for N, 92 d for P2O5 and 85 d for K2O after emergence, respectively. Total nutrient accumulation of N, P2O5 and K2O was 385.8, 244. 7 and 340.3 kg ha-1, respectively. Approximately 12. 5 kg N, 8. 0 kg P2O5 and 11.1 kg K2O were needed for producing 100 kg lint with the leaves and stems under the super high yield condition of 3 000 kg ha-1 in Xinjiang.展开更多
The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring ...The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.展开更多
文摘Flow records for stations in the Casamance basin are incomplete. Several gaps were noted over the 1980-2021 study period, making this study tedious. The aim of this study is to assess the potential impact of climate change on the flow of the Casamance watershed at Kolda. To this end, hydrological series are simulated and then extended using the GR2M rainfall-runoff model, with a monthly time step. Projected climate data are derived from a multi-model ensemble under scenarios SSP2-4.5 (scenario with additional radiative forcing of 4.5 W/m<sup>2</sup> by 2099) and SSP5-8.5 (scenario with additional radiative forcing of 8.5 W/m<sup>2</sup> by 2099). An analysis of the homogeneity of the rainfall data series from the Kolda station was carried out using KhronoStat software. The Casamance watershed was then delimited using ArcGIS to determine the morphometric parameters of the basin, which will be decisive for the rest of the work. Next, monthly evapotranspiration was calculated using the formula proposed by Oudin et al. This, together with rainfall and runoff, forms the input data for the model. The GR2M model was then calibrated and cross-validated using various simulations to assess its performance and robustness in the Casamance watershed. The version of the model with the calibrated parameters will make it possible to extend Casamance river flows to 2099. This simulation of future flows with GR2M shows a decrease in the flow of the Casamance at Kolda with the two scenarios SSP2-4.5 and SSP5-8.5 during the rainy period, and almost zero flows during the dry season from the period 2040-2059.
文摘Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.
文摘A quantum model based on solutions to the Schrodinger-Poisson equations is developed to investigate the device behavior related togate tunneling current for nanoscale MOSFETs with high-k gate stacks. This model can model various MOS device structures with combinations of high-k dielectric materials and multilayer gate stacks,revealing quantum effects on the device performance. Comparisons are made for gate current behavior between nMOSFET and pMOSFET high- k gate stack structures. The results presented are consistent with experimental data, whereas a new finding for an optimum nitrogen content in HfSiON gate dielectric requires further experimental verifications.
基金Project (51775481) supported by the National Natural Science Foundation of ChinaProject (E2019203418) supported by the Natural Science Foundation of Hebei Province, ChinaProject (ZD2017078) supported by the Science and Technology Plan of Hebei Higher School of Education Department, China。
文摘In order to effectively predict the fracture of AA7075-T6 sheet, the forming limit curves of AA7075-T6 high-strength sheet were drawn according to Morciniak Kuczyski (M K) model and Lou Huh criterion, respectively. The errors between the predicted values of the two theoretical prediction models and experimental values were calculated by error analysis. The forming limit curves were verified by the punch stretch test to evaluate the prediction accuracy of M K model and Lou Huh criterion. The error analysis results show that the mean error of Lou Huh criterion with the optimal parameters for all tensile specimens is 25.04%, while the mean error of M K model for all tensile specimens is 74.24%. The prediction accuracy of Lou Huh criterion in predicting the fracture of AA7075-T6 sheet is higher. The punch stretch test results show that the forming limit curve drawn by Lou Huh criterion can effectively predict the fracture of AA7075-T6 sheet, but the prediction accuracy of M K model is relatively poor.
文摘Objective To establish an effective assay to access the effects of natural products on cathepsin K for screening antiosteoporosis drugs. Methods To obtain the purified cathepsin K, we cloned the target fragment from the mRNA of human osteosacoma cell line MG63 and demonstrated its correctness through DNA sequencing. Cathepsin K was expressed in a high amount in E.coli after IPTG induction, and was purified to near homogenetity through resolution and column purification. The specificity of the protein was shown by Western blotting experiment. The biological activity of the components in the fermentation broth was assayed by their inhibitory effects on cathepsin K and its analog papain. Results With the inhibition of papain activity as a screen index, the fermentation samples of one thousand strains of fungi were tested and 9 strains among them showed strong inhibitory effects. The crude products of the fermentation broth were tested for their specific inhibitory effects on the purified human cathepsin K, the product of fungi 2358 shows the highest specificity against cathepsin K. Conclusions The compounds isolated from fungi 2358 show the highest biological activity and are worth further structure elucidation and function characterization.
文摘为快速识别冒犯性评论文本中的用户热点主题,解决传统主题模型在处理评论文本时语义描述不充分、上下文信息丢失和主题连贯性不强,以及K-means聚类算法对K值和初始中心点敏感的问题。使用CoSENT(cosine sentence)模型获取包含冒犯性语言的评论文本的句子级向量特征,对通过统一流形逼近与投影算法即UMAP(uniform manifold approximation and projection)模型降维后的向量矩阵使用基于Canopy+的改进K-means算法进行类簇划分,用(class term frequency-inverse document frequency,c-TF-IDF)识别各主题簇的主题特征,进行主题建模。通过对比冒犯性评论文本数据集以及普通评论数据集的实验验证了方法有效性。结果表明本文方法能够得到更好的主题一致性。
基金supported by the National Key Technologies R&D Program in 10th Five-year Plan of China(2001BA507A)the National Natural Sicence Foundation of China(39760040).
文摘The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of N, P2O5, K2O in cotton leaves, stems, squares and bolls decreased obviously with the time over the whole growth duration and the falling extent was greater in high-yield cotton than in CK. Contents of N in leaves, squares and bolls, in particular in the leaves of fruit-bearing shoot was higher in high-yield cotton than in CK. Contents of P2O5 in squares and bolls and that of K2O in stems were higher in high-yield cotton than in CK during the whole growing period. The accumulations of N, P2O5 and K2O in the cotton plants could be described with a logistic curve equation. There was the fastest nutrient uptake at about 90 d for N, 92 d for P2O5 and 85 d for K2O after emergence, respectively. Total nutrient accumulation of N, P2O5 and K2O was 385.8, 244. 7 and 340.3 kg ha-1, respectively. Approximately 12. 5 kg N, 8. 0 kg P2O5 and 11.1 kg K2O were needed for producing 100 kg lint with the leaves and stems under the super high yield condition of 3 000 kg ha-1 in Xinjiang.
基金supported by the National Natural Science Foundation of China(Grant No.51979224)the China National Funds for Distinguished Young Scientists(Grant No.52125904).
文摘The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.
