Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis...Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.展开更多
识别我国碳价影响因素并分析其对碳价的预测作用,对助推经济社会发展的全面绿色转型具有重要的理论和现实意义。本文以我国碳排放配额成交量和成交额最大的湖北碳排放权交易市场碳价为样本,对可能影响我国碳价的相关因素进行梳理,从五...识别我国碳价影响因素并分析其对碳价的预测作用,对助推经济社会发展的全面绿色转型具有重要的理论和现实意义。本文以我国碳排放配额成交量和成交额最大的湖北碳排放权交易市场碳价为样本,对可能影响我国碳价的相关因素进行梳理,从五个维度筛选出九个重要影响因素,同时运用各类经典预测模型和动态模型选择(Dynamic model selection,DMS)及动态模型平均(Dynamic model averaging,DMA)方法对我国碳价进行了预测对比研究,并分析了各类影响因素预测作用的时变特征。结果表明:一方面,在所选五类影响因素中,经济形势、金融市场走势、国际碳价和大气环境对我国碳价的影响较大,且可以提供较好的预测作用;而国际化石能源价格对我国碳价的影响力在逐步下降。另一方面,与传统计量模型相比,DMS可以为我国碳价提供更高的预测精度。上述结论可以为我国政府监管政策的制定和相关企业的碳交易决策提供参考。展开更多
基金The National Natural Science Foundation of China(No.30900356,81071135)
文摘Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.
文摘识别我国碳价影响因素并分析其对碳价的预测作用,对助推经济社会发展的全面绿色转型具有重要的理论和现实意义。本文以我国碳排放配额成交量和成交额最大的湖北碳排放权交易市场碳价为样本,对可能影响我国碳价的相关因素进行梳理,从五个维度筛选出九个重要影响因素,同时运用各类经典预测模型和动态模型选择(Dynamic model selection,DMS)及动态模型平均(Dynamic model averaging,DMA)方法对我国碳价进行了预测对比研究,并分析了各类影响因素预测作用的时变特征。结果表明:一方面,在所选五类影响因素中,经济形势、金融市场走势、国际碳价和大气环境对我国碳价的影响较大,且可以提供较好的预测作用;而国际化石能源价格对我国碳价的影响力在逐步下降。另一方面,与传统计量模型相比,DMS可以为我国碳价提供更高的预测精度。上述结论可以为我国政府监管政策的制定和相关企业的碳交易决策提供参考。
