本文将研究贝叶斯法则视角下的空间自相关误差自相关模型(Spatial Autoregressive Model with Autoregressive Disturbances,SARAR模型)变量选择问题。通过将基于BIC准则的子集选择法推广到空间模型,实现SARAR模型的变量选择,并证明在...本文将研究贝叶斯法则视角下的空间自相关误差自相关模型(Spatial Autoregressive Model with Autoregressive Disturbances,SARAR模型)变量选择问题。通过将基于BIC准则的子集选择法推广到空间模型,实现SARAR模型的变量选择,并证明在一定条件下,对于SARAR模型的变量选择BIC准则具有良好的渐近性质。同时本文还将利用Monte Carlo模拟验证BIC准则能够很好的实现SARAR模型的变量选择。最后以股票收益率为例,在验证股票收益率具有空间效应的前提下,利用BIC准则对影响股票收益率的众多财务指标进行变量选择。展开更多
Due to the instructive role of the peristaltic phenomenon in the human body, interests have been developed in recent years towards peristaltic transport with various thermo-physical features.The current investigation ...Due to the instructive role of the peristaltic phenomenon in the human body, interests have been developed in recent years towards peristaltic transport with various thermo-physical features.The current investigation reveals the effects of the magnetic field and variable transport properties on the peristaltic transport of a Casson fluid slip flow through an inclined channel.Nonlinear coupled partial differential equations regulate the fluid flow. Through the perturbation method, the momentum and energy equations are solved for small values of variable viscosity and thermal conductivity, and the closed-form solution is obtained for mass transfer. The impact on physiological quantities of related parameters of interest is evaluated and discussed via graphs. The results obtained for the current flow represent some interesting behaviors which have applications in the biomedical field.展开更多
Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-s...Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.展开更多
【目的】由适时获得的高光谱数据代替传统繁琐的实验室土壤养分测定数据来进行变量施肥,实现冬小麦高产优质的目标。【方法】本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数(OSAVI,optimiza...【目的】由适时获得的高光谱数据代替传统繁琐的实验室土壤养分测定数据来进行变量施肥,实现冬小麦高产优质的目标。【方法】本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数(OSAVI,optimization of soil-adjusted vegetation index)和变量施肥模型进行变量施肥管理(变量区),以相邻地块常规非变量(均一)施肥区(对照区)为对照,研究了不同氮肥处理冬小麦冠层光谱特征及其施肥效应。【结果】变量施肥之后两种氮肥处理在敏感波段670nm和760～900nm处反射率差异明显,而670nm和760～900nm是氮素和冠层的敏感波段,说明进行变量施肥时,利用基于这两个波段组合的光谱指数OSAVI优于其它波段组合的光谱指数;OSAVI不同生育时期的变化情况,反映了变量施肥在调控作物长势及群体结构上的优势;与对照区相比变量区提高产量达378.72kg·ha-1,并降低了各小区产量之间的变异,变量区土壤硝态氮浓度降低,氮肥利用率提高,生态效益较为明显。【结论】该技术通过改善冬小麦群体质量,延缓了植株衰老,促进干物质和氮积累,增加冬小麦产量和氮肥利用率。展开更多
文摘本文将研究贝叶斯法则视角下的空间自相关误差自相关模型(Spatial Autoregressive Model with Autoregressive Disturbances,SARAR模型)变量选择问题。通过将基于BIC准则的子集选择法推广到空间模型,实现SARAR模型的变量选择,并证明在一定条件下,对于SARAR模型的变量选择BIC准则具有良好的渐近性质。同时本文还将利用Monte Carlo模拟验证BIC准则能够很好的实现SARAR模型的变量选择。最后以股票收益率为例,在验证股票收益率具有空间效应的前提下,利用BIC准则对影响股票收益率的众多财务指标进行变量选择。
文摘Due to the instructive role of the peristaltic phenomenon in the human body, interests have been developed in recent years towards peristaltic transport with various thermo-physical features.The current investigation reveals the effects of the magnetic field and variable transport properties on the peristaltic transport of a Casson fluid slip flow through an inclined channel.Nonlinear coupled partial differential equations regulate the fluid flow. Through the perturbation method, the momentum and energy equations are solved for small values of variable viscosity and thermal conductivity, and the closed-form solution is obtained for mass transfer. The impact on physiological quantities of related parameters of interest is evaluated and discussed via graphs. The results obtained for the current flow represent some interesting behaviors which have applications in the biomedical field.
基金supported by the USDA National Institute of Food and Agriculture,Hatch project 1019654.
文摘Crop models are widely used to predict plant growth,water input requirements,and yield.However,existing models are very complex and require hundreds of variables to perform accurately.Due to these shortcomings,large-scale applications of crop models are limited.In order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop yields.In addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature Extraction.The reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables.To establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals.Specifically,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by 80-fold.This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.
文摘【目的】由适时获得的高光谱数据代替传统繁琐的实验室土壤养分测定数据来进行变量施肥,实现冬小麦高产优质的目标。【方法】本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数(OSAVI,optimization of soil-adjusted vegetation index)和变量施肥模型进行变量施肥管理(变量区),以相邻地块常规非变量(均一)施肥区(对照区)为对照,研究了不同氮肥处理冬小麦冠层光谱特征及其施肥效应。【结果】变量施肥之后两种氮肥处理在敏感波段670nm和760～900nm处反射率差异明显,而670nm和760～900nm是氮素和冠层的敏感波段,说明进行变量施肥时,利用基于这两个波段组合的光谱指数OSAVI优于其它波段组合的光谱指数;OSAVI不同生育时期的变化情况,反映了变量施肥在调控作物长势及群体结构上的优势;与对照区相比变量区提高产量达378.72kg·ha-1,并降低了各小区产量之间的变异,变量区土壤硝态氮浓度降低,氮肥利用率提高,生态效益较为明显。【结论】该技术通过改善冬小麦群体质量,延缓了植株衰老,促进干物质和氮积累,增加冬小麦产量和氮肥利用率。
